Showing papers in "Annals of Operations Research in 2017"

Green supply chain network design with stochastic demand and carbon price

Abstract: This paper presents a two-stage stochastic programming model to design a green supply chain in a carbon trading environment. The model solves a discrete location problem and determines the optimal material flows and the number of carbon credits/allowances traded. The study contributes to the existing literature by incorporating uncertainty in carbon price and product demand. The proposed model is applied to a real world case study and the numerical results are carefully analyzed and interpreted. We find that the supply chain configuration can be highly sensitive to the probability distribution of the carbon credit price. More importantly, we observe that carbon price and budget availability for supply chain reconfiguration can both have a positive but nonlinear relationship with greening of the supply chain.

Optimal control in epidemiology

Abstract: Mathematical modelling of infectious diseases has shown that combinations of isolation, quarantine, vaccine and treatment are often necessary in order to eliminate most infectious diseases. However, if they are not administered at the right time and in the right amount, the disease elimination will remain a difficult task. Optimal control theory has proven to be a successful tool in understanding ways to curtail the spread of infectious diseases by devising the optimal diseases intervention strategies. The method consists of minimizing the cost of infection or the cost of implementing the control, or both. This paper reviews the available literature on mathematical models that use optimal control theory to deduce the optimal strategies aimed at curtailing the spread of an infectious disease.

Typology and literature review for dial-a-ride problems

Abstract: Dial-a-ride problems consist of designing vehicle routes and time schedules in a system of demand-dependent, collective people transportation In the standard problem, operational costs are minimized, subject to full demand satisfaction and service level requirements However, to enhance the practical applicability of solution methods, authors increasingly focus on problem variants that adopt additional real-life characteristics First, this work introduces an up-to-date classification that distinguishes multiple categories of real-life characteristics Second, the wide range of solution methods proposed in the literature is reviewed in a structured manner Although the existing literature is reviewed exhaustively, specific attention is devoted to recent developments Third, an extensive overview table provides full details on all problem characteristics and solution methods applied in each paper discussed Fourth, lacunae in research conducted to date and opportunities for future work are identified

Carbon emission quota allocation among China’s industrial sectors based on the equity and efficiency principles

Abstract: The carbon emission of China’s industry accounts for more than 70 % of the total in the nation, thus the implementation of carbon emission quota trading in industry is of great importance to realize China’s national carbon emission reduction targets. Meanwhile, the allocation of carbon emission quota among sectors or enterprises proves the first and critical step. For this reason, this paper constructs a comprehensive index combined with the subjective, objective and linear combination weighting methods to allocate carbon emission quotas among the 39 sectors of China’s industry in 2020 based on the level of 2015, and employs the input-oriented ZSG-DEA model to examine the efficiency of allocation solutions in 2020. The results indicate that, first, when carbon emission reduction capacity, responsibility and potential are considered for the comprehensive index of carbon emission quota allocation, the mitigation responsibility plays a relatively higher role than other two indicators. Second, all of the subjective, objective and linear combination weighting methods can be used for effective allocation of carbon emission quotas, and the former two methods have less advantage in light of efficiency. Third, six key industrial sectors are respectively allocated over 500 million tonnes of carbon emission quotas in 2020, which together account for 91.77 % of the total in the industry. Finally, the final carbon emission quota allocation solution reflects both the equity and efficiency principles and achieve the Pareto optimal state.

A survey of the standard location-routing problem

Abstract: In this paper, we define the standard LRP as a deterministic, static, discrete, single-echelon, single-objective location-routing problem in which each customer (vertex) must be visited exactly once for the delivery of a good from a facility, and in which no inventory decisions are relevant. We review the literature on the standard LRP published since the survey by Nagy and Salhi appeared in 2006. We provide concise paper excerpts that convey the central ideas of each work, discuss recent developments in the field, provide a numerical comparison of the most successful heuristic algorithms, and list promising topics for further research.

An inventory model under price and stock dependent demand for controllable deterioration rate with shortages and preservation technology investment

Abstract: This paper develops an EOQ inventory model that considers the demand rate as a function of stock and selling price. Shortages are permitted and two cases are studied: (i) complete backordering and (ii) partial backordering. The inventory model is for a deteriorating seasonal product. The product’s deterioration rate is controlled by investing in the preservation technology. The main purpose of the inventory model is to determine the optimum selling price, ordering frequency and preservation technology investment that maximizes the total profit. Additionally, the paper proves that the total profit is a concave function of selling price, ordering frequency and preservation technology investment. Therefore, a simple algorithm is proposed to obtain the optimal values for the decision variables. Several numerical examples are solved and studied along with a sensitivity analysis.

Low-carbon supply policies and supply chain performance with carbon concerned demand

Abstract: A dilemma between economic growth and environmental deterioration has never been recognized so seriously until the environmental problem has been impacting everyone’s daily life cogently (e.g., the serious fog and haze in China). Simultaneously, the rising environment awareness of consumers leads to the close attention to the product’s carbon performance reflected in the carbon concerned demand, which provides opportunity to rebuild our business ecosystem. The paper expands the environment view to supply chain operations. A Stackelberg-like model is developed to game-theoretically analyze the decentralized decisions of the manufacturer and retailer. Low-carbon effort is brought into decision by both sides. The centralized decision-making is also investigated as a benchmark to evaluate the supply chain performance. Additionally,we propose a carbon-related price–discount sharing-like scheme to achieve the channel coordination and discuss the possibility of Pareto improvement. Several interesting managerial insights on low-carbon factors are concluded.

Robust supply chain network design: an optimization model with real world application

Abstract: This paper presents a robust optimization model for the design of a supply chain facing uncertainty in demand, supply capacity and major cost data including transportation and shortage cost parameters. We first present a base model that aims to determine the strategic ‘location’ and tactical ‘allocation’ decisions for a deterministic four-tier supply chain. The model is then extended to incorporate uncertainty in key input parameters using a robust optimization approach that can overcome the limitations of scenario-based solution methods in a tractable way, i.e. without excessive changes in complexity of the underlying base deterministic model. The application of the approach is investigated in an actual case study where real data is utilized to design a bread supply chain network. Numerical results obtained from model implementation and sensitivity analysis experiments arrive at important managerial insights and practical implications.

Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs

Abstract: Chinese industry has developed greatly since China implemented its “reform and opening-up” policy in 1978. With the rapid development of industry, the problems of growing energy consumption and environmental pollution are drawing increasing attention from government managers and scholars. This paper divides industrial systems into two stages, an energy utilization stage and a pollution treatment stage, for accurately evaluating the total-factor energy efficiency as well as the overall efficiency. We build a new two-stage data envelopment analysis model with shared inputs to open the “black box” of efficiency measurement in traditional energy efficiency methods. Applying the model to data for Chinese regions, we can display the advantages and disadvantages of these two stages of industry. The results show that (1) the performance of Chinese industry improved during the years 2006–2010; (2) the energy utilization stage performance was better than that of the pollution treatment stage, but the gaps reduced year by year; and (3) energy efficiency increased during this period. Based on these results, some policy recommendations are given.

Credit financing in economic ordering policies for non-instantaneous deteriorating items with price dependent demand and two storage facilities

Abstract: The formulation of classical deteriorating inventory models is done with the common unrealistic assumption that all the items start deteriorating as soon as they arrive in the warehouse. On the contrary, in a realistic environment, it has been observed that there are several items that do not deteriorate immediately. Items such as dry fruits, potatoes, yams and even some fruits and vegetables have a shelf life and deteriorate only after a time lag. Apart from this, in today’s competitive business world, the supplier usually offers a trade credit period to his retailers to attract more sales and the retailer considers it as an alternative to price discount. The present research proposes a two warehouse inventory model for non-instantaneous deteriorating items under trade credit based on the above phenomena, where the demand rate is assumed to be a function of the selling price. Further, shortages are completely backlogged and the interest on shortages at the beginning of the cycle has also been considered. The objective of the study is to determine the retailer’s optimal replenishment policies that maximize the average profit per unit time. Conclusively, a numerical example is presented to illustrate the applicability of the proposed model. Sensitivity analysis on key parameters is provided to reveal the managerial insights.

Remanufacturing supply chain coordination under the stochastic remanufacturability rate and the random demand

Abstract: As an effective mode for resource recovery, remanufacturing has been widely recognized in practice and academia. However, coordination is needed and multi-uncertainties exist in a remanufacturing supply chain (RSC). Under a retailer collection mode, this paper extends the existing studies on a revenue-sharing mechanism for a forward supply chain to examine how to coordinate a RSC between a remanufacturer and a retailer by developing a mathematical model. This model considers two types of uncertainties, they are, the stochastic remanufacturability rate from the supply side of used products and the random demand occurring in remarketing of remanufactured products. This study fills the research gap on RSC coordination under the multi-uncertainty environment. Moreover, it introduces an iterative algorithm (the Newton–Raphson Method) to deal with difficulty in solving the implicit function of the payment to consumers under the non-uniform demand distribution by finding the approximate value. The research results show that a revenue-sharing contract for a RSC with multi-uncertainties can increase profit for the whole RSC as well as the remanufacturer and the retailer by eliminating double marginalization. Besides, the government subsidy to the remanufacturer can motivate the retailer to collect more used products under a revenue-sharing case since the retailer can share benefits of the whole RSC. A case study of remanufactured truck engines demonstrates benefits of the proposed revenue-sharing mechanism and the profit increase for the whole RSC with the government subsidy.

Environmental subsidy and the choice of green technology in the presence of green consumers

Abstract: In this paper, we present a study on a government using subsidy policy to motivate firms’ adoption of green emissions-reducing technology when consumers are environmentally discerning. We consider two profit-maximizing firms selling two products in a price and pollution sensitive market. The products differ only in their manufacturing costs, selling prices and the amount of pollutant emissions per unit of product. The objective of each firm is to determine the selling prices of the products, taking into account the impact of green technology on costs and customer demands. Two cases are considered: (1) the government has limited budget and can choose only one firm at most to provide subsidy; (2) the government has sufficient budget and can choose both firms to provide subsidy. We discuss which firm should be selected in each case and in which situation the firm has incentive to invest in the green technology. We also show that the green technology level, environmental improvement coefficient and unit cost increase coefficient play important roles in the government subsidy strategy.

Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds

Abstract: Development and application of the data envelopment analysis (DEA) method, have been the subject of numerous reviews. In this paper, we consider the papers that apply DEA methods specifically to financial services, or which use financial services data to experiment with a newly introduced DEA model. We examine 620 papers published in journals indexed in the Web of Science database, from 1985 to April 2016. We analyse the sample applying citations network analysis. This paper investigates the DEA method and its applications in financial services. We analyse the diffusion of DEA in three sub-samples: (1) banking groups, (2) money market funds, and (3) insurance groups by identifying the main paths, that is, the main flows of the ideas underlying each area of research. This allows us to highlight the main approaches, models and efficiency types used in each research areas. No unique methodological preference emerges within these areas. Innovations in the DEA methodologies (network models, slacks based models, directional distance models and Nash bargaining game) clearly dominate recent research. For each subsample, we describe the geographical distribution of these studies, and provide some basic statistics related to the most active journals and scholars.

Option pricing and coordination in the fresh produce supply chain with portfolio contracts

Abstract: This paper studies a fresh produce supply chain that consists of a supplier and a retailer in a newsvendor framework. The supplier is the Stackelberg leader and the retailer is the follower. The retailer can obtain products from the supplier by wholesale price and call option portfolio contracts. The fresh produce incurs a circulation loss in quantity during its transportation. The retailer’s optimal ordering policy and the supplier’s optimal pricing policy are derived in the presence of portfolio contracts and circulation loss. It is demonstrated that, as the prices of option increase toward their optimal, the supplier’s expected profit increases whereas the retailer’s expected profit decreases, and the retailer is more sensitive to the price change. It is also found that the fresh produce supply chain can be coordinated by the portfolio contracts, and Pareto improvement for both chain members can also be achieved as compared with the non-coordinated contracts. However, when the supply chain is coordinated, the supplier cannot realize its optimal pricing strategy. Finally, it is shown that the supplier’s optimal option pricing policy is independent to the demand risk and wholesale price, and the circulation loss of fresh produce increases the management risks of the fresh produce supply chain.

Conic scalarization approach to solve multi-choice multi-objective transportation problem with interval goal

Abstract: This paper explores the study of multi-choice multi-objective transportation problem (MCMTP) under the light of conic scalarizing function. MCMTP is a multi-objective transportation problem (MOTP) where the parameters such as cost, demand and supply are treated as multi-choice parameters. A general transformation procedure using binary variables is illustrated to reduce MCMTP into MOTP. Most of the MOTPs are solved by goal programming (GP) approach, but the solution of MOTP may not be satisfied all times by the decision maker when the objective functions of the proposed problem contains interval-valued aspiration levels. To overcome this difficulty, here we propose the approaches of revised multi-choice goal programming (RMCGP) and conic scalarizing function into the MOTP, and then we compare among the solutions. Two numerical examples are presented to show the feasibility and usefulness of our paper. The paper ends with a conclusion and an outlook on future studies.

Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review

Abstract: Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (Manag Sci 2:138–151, 1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and computational improvements have greatly contributed to the diverse applications and several variants of GP models. In this paper we present a state of the art literature review on GP applications in three selected (prominent and popular) areas, namely engineering, management and social sciences.

How supply chain coordination affects the environment: a carbon footprint perspective

Abstract: Environmental responsibility has become an important part of doing business. Government regulations and customers’ increased awareness of environmental issues are pushing supply chain entities to reduce the negative influence of their operations on the environment. In today’s world, companies must assume joint responsibility with their suppliers for the environmental impact of their actions. In this paper, we study coordination between a buyer and a vendor under the existence of two emission regulation policies: cap-and-trade and tax. We investigate the impact of decentralized and centralized replenishment decisions on total carbon emissions. The buyer in this system faces a deterministic and constant demand rate for a single product in the infinite horizon. The vendor produces at a finite rate and makes deliveries to the buyer on a lot-for-lot basis. Both the buyer and the vendor aim to minimize their average annual costs resulting from replenishment set-ups and inventory holding. We provide decentralized and centralized models for the buyer and the vendor to determine their ordering/production lot sizes under each policy. We compare the solutions due to independent and joint decision-making both analytically and numerically. Finally, we arrive at coordination mechanisms for this system to increase its profitability. However, we show that even though such coordination mechanisms help the buyer and the vendor decrease their costs without violating emission regulations, the cost minimizing solution may result in increased carbon emission under certain circumstances.

A supply chain network game theory model of cybersecurity investments with nonlinear budget constraints

Abstract: In this paper, we develop a supply chain network game theory model consisting of retailers and demand markets with retailers competing noncooperatively in order to maximize their expected profits by determining their optimal product transactions as well as cybersecurity investments subject to nonlinear budget constraints that include the cybersecurity investment cost functions. The consumers at the demand markets reflect their preferences through the demand price functions, which depend on the product demands and on the average level of cybersecurity in the supply chain network. We identify the supply chain network vulnerability to cyberattacks as well as that of the individual retailers. We demonstrate that the governing Nash equilibrium conditions can be formulated as a variational inequality problem and we provide a novel alternative formulation, along with the accompanying theory. We also propose an algorithm for the alternative formulation, which yields, at each iteration, closed form expressions in product transactions, security levels, and Lagrange multipliers associated with the budget constraints. We then apply the algorithm to compute solutions to a spectrum of numerical supply chain network cybersecurity investment examples. The examples broaden our understanding of the impacts of the addition of retailers, changes in budgets, demand price functions, and financial damages, on equilibrium product transactions and cybersecurity investments, as well as on the supply chain network vulnerability and retailer vulnerability under budget constraints.

Basic theoretical foundations and insights on bilevel models and their applications to power systems

Abstract: Decision making in the operation and planning of power systems is, in general, economically driven, especially in deregulated markets. To better understand the participants’ behavior in power markets, it is necessary to include concepts of microeconomics and operations research in the analysis of power systems. Particularly, game theory equilibrium models have played an important role in shaping participants’ behavior and their interactions. In recent years, bilevel games and their applications to power systems have received growing attention. Bilevel optimization models, Mathematical Program with Equilibrium Constraints and Equilibrium Problem with Equilibrium Constraints are examples of bilevel games. This paper provides an overview of the full range of formulations of non-cooperative bilevel games. Our aim is to present, in an unified manner, the theoretical foundations, classification and main techniques for solving bilevel games and their applications to power systems.

Studying a set of properties of inconsistency indices for pairwise comparisons

Abstract: Pairwise comparisons between alternatives are a well-established tool to decompose decision problems into smaller and more easily tractable sub-problems. However, due to our limited rationality, the subjective preferences expressed by decision makers over pairs of alternatives can hardly ever be consistent. Therefore, several inconsistency indices have been proposed in the literature to quantify the extent of the deviation from complete consistency. Only recently, a set of properties has been proposed to define a family of functions representing inconsistency indices. The scope of this paper is twofold. Firstly, it expands the set of properties by adding and justifying a new one. Secondly, it continues the study of inconsistency indices to check whether or not they satisfy the above mentioned properties. Out of the four indices considered in this paper, in their present form, two fail to satisfy some properties. An adjusted version of one index is proposed so that it fulfills them.

Dual-channel supply chain decisions under asymmetric information with a risk-averse retailer

Abstract: Many upstream brand manufacturers have established their direct channels to compete with the retailers; this is encroachment behavior by the manufacturers. In this paper, we introduce the retailer’s risk-averse behavior into the manufacturer’s encroachment problem under asymmetric information, focusing specifically on how the risk-averse behavior of the retailer and the per-unit selling cost of the manufacturer influence the optimal decisions. To address this problem, we assume that the market demand may be high or low, and each kind of demand follows a truncated normal distribution. The retailer has more information regarding the market size than the manufacturer. Under the mean–variance decision framework, we develop a dual-channel supply chain model and obtain three feasible regions of the optimal equilibrium results. We find that whether the profit of the manufacturer and the utility of the retailer are better off or worse off depends on the manufacturer’s per-unit selling cost and the degree of risk aversion of the retailer. Numerical experiments provide the comparisons of the expected profits and the utilities of both members in the supply chain under asymmetric information and symmetric information.

Closed-loop supply chain coordination through incentives with asymmetric information

Abstract: A closed-loop supply chain seeks to enhance the consumers’ environmental consciousness to increase both the profits and the return of past-sold products. Even though, firms have misaligned interests for closing the loop: while all firms exploit consumers environmental consciousness to increase sales, only manufacturers use it for appropriating of returns’ residual value. Starting from a benchmark (no-incentive) scenario where a manufacturer (M) is the leader and a retailer (R) is the follower, we develop two incentive games through a profit-sharing contract to align firms’ motivations for closing the loop. In both incentive games, the incentive takes the form of a share of profits that M transfers to R. Our question is how the sharing fraction should be determined to make both players economically better-off. The first incentive game assumes that R has no-information on the sharing parameter, which is determined by M after R sets her strategies; thus the incentive has an endogenous nature. In the second incentive game the sharing parameter is common knowledge and both players know its values before the game starts, thus the incentive has an exogenous nature. We find that an endogenous incentive is never more economically and environmentally convenient than a no-incentive game. In contrast, an exogenous incentive can make both players economically better-off inside specific sharing parameter ranges. Nevertheless, when other forces (e.g., competition or legislation) impose the adoption of a profit-sharing contract, M should supply an endogenous incentive when the exogenous share is either too high or too low.

A multi-objective, hub-and-spoke model to design and manage biofuel supply chains

Abstract: In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants relies on a hub-and-spoke structure which optimizes transportation costs of biomass. The model proposed optimizes the $$\hbox {CO}_{2}$$ emissions due to transportation-related activities in the supply chain. The model also optimizes the social impact of biofuels. The social impacts are evaluated by the number of jobs created. The multi-objective optimization model is solved using an augmented $$\epsilon $$ -constraint method. The method provides a set of Pareto optimal solutions. We develop a case study using data from the Midwest region of the USA. The numerical analyses estimates the quantity and cost of cellulosic ethanol delivered under different scenarios generated. The insights we provide will help policy makers design policies which encourage and support renewable energy production.

Integrated forward/reverse logistics network design under uncertainty with pricing for collection of used products

Abstract: This paper addresses design and planning of an integrated forward/reverse logistics network over a planning horizon with multiple tactical periods. In the network, demand for new products and potential return of used products are stochastic. Furthermore, collection amounts of used products with different quality levels are assumed dependent on offered acquisition prices to customer zones. A uniform distribution function defines the expected price of each customer zone for one unit of each used product. Using two-stage stochastic programming, a mixed-integer linear programming model is proposed. To cope with demand and potential return uncertainty, Latin Hypercube Sampling method is applied to generate fan of scenarios and then, backward scenario reduction technique is used to reduce the number of scenarios. Due to the problem complexity, a novel simulation-based simulated annealing algorithm is developed to address large-sized test problems. Numerical results indicate the applicability of the model as well as the efficiency of the solution approach. In addition, the performance of the scenario generation method and the importance of stochasticity are examined for the optimization problem. Finally, several numerical experiments including sensitivity analysis on main parameters of the problem are performed.

Modelling beneficiaries’ choice in disaster relief logistics

Abstract: This paper introduces the element of beneficiaries’ choice into a location-routing problem for disaster relief logistics suited for decision support systems. Decision makers in humanitarian logistics face the challenge where to establish distribution centers (DCs) for relief goods. For this purpose, two objectives are considered: the impact of the relief operations on the beneficiaries and the efficient use of monetary resources. The proposed multi-objective location-routing model minimizes unserved demand as well as cost for opening DCs and for routing relief goods. It anticipates the choice of beneficiaries to which DC to go (if at all), based on a model adopted from the literature on competitive location analysis. A mathematical programming formulation is presented. For small instances, the Pareto front can be determined exactly using an epsilon constraint method. For solving also realistic instances, an evolutionary algorithm has been implemented and evaluated. The algorithms are tested on real-world instances from Mozambique. The results show that when designing a distribution network, improvements can be achieved by taking the predicted behavior of beneficiaries into account.

Bounded and discrete data and Likert scales in data envelopment analysis: application to regional energy efficiency in China

Abstract: In data envelopment analysis (DEA), it is usually assumed that all data are continuous and not restricted by upper and/or lower bounds. However, there are situations where data are discrete and/or bounded, and where projections arising from DEA models are required to fall within those bounds. Such situations can be found, for example, in cases where percentage data are present and where projected percentages must not exceed the requisite 100 % limit. Other examples include Likert scale data. Using existing integer DEA approaches as a backdrop, the current paper presents models for dealing with bounded and discrete data. Our proposed models address the issue of constraining DEA projections to fall within imposed bounds. It is shown that Likert scale data can be modeled using the proposed approach. The proposed DEA models are used to evaluate the energy efficiency of 29 provinces in China.

Risk hedging via option contracts in a random yield supply chain

Abstract: This article investigates the role of option contracts in a random yield supply chain in the presence of a spot market. Considering a single-period supplier-manufacturer system where the supplier with random yield produces key components for the manufacturer and the manufacturer assembles/processes the components into end products to meet a deterministic market demand, we develop game models to derive the manufacturer’s optimal ordering policy and the supplier’s optimal production policy under two contract mechanisms (with and without option contracts). Our results suggest that option contracts can coordinate the manufacturer’s order quantity as well as the supplier’s production quantity, and eventually achieve optimal supply chain performance, i.e. the random yield supply chain can be completely coordinated with option contracts in our setting. However, our study also reveals that the supplier and the manufacturer are not always better off with option contracts than without. Therefore, the conditions on which Pareto-improvement is achieved are provided in this paper. Finally, by adopting numerical examples, we draw additional managerial insights into managing random yield supply chains in the presence of spot market.

Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors

Abstract: The growing concern for sustainability has forced the researchers and managers to incorporate the environmental and social factors along with the economical factors in the design of supply chains. This paper presents the design and optimization of a multi-objective closed-loop supply chain considering the economical and environmental factors with uncertainty in parameters. The proposed network is modeled as fuzzy multi-objective mixed integer linear programming problem considering multi-customer zones, multi-collection centers, multi-disassembly centers, multi-refurbishing centers, multi-external suppliers, and different product recovery processes; to take care for purchasing cost, transportation cost, processing cost, set-up cost, and capacity constraints simultaneously. The model is solved using an interactive $$\upvarepsilon $$ -constraint method. A case example is solved using LINGO 14.0 to demonstrate the significance and applicability of the developed fuzzy optimization model for closed-loop supply chain.

Integrating disassembly line balancing in the planning of a reverse logistics network from the perspective of a third party provider

Abstract: The increasing pressure due to legislation and various public policies has prompted manufacturers to seriously take into consideration the impact of electronic waste. To overcome this challenge, several manufacturers have incorporated reverse logistics into their business lines. Due to a lack of resources and/or managerial expertise, several companies choose to outsource product recovery activities to third parties. The growing demand has given rise to the number of third party logistics providers, who are assigned the task of efficiently and cost effectively achieving product recovery. The work of the 3PRL’s commences with the collection of returned products from various companies to retrieve maximum value harvested in the form of reusable products, components and materials. Thus, a 3PRLP needs to establish a recovery process able to effectively retrieve value from various types of product. To deal with multiple types of products, the recovery network needs to work with a balanced disassembly line. Thus in this paper we proposes a cost effective reverse logistics network from the perspective of the third party, which integrates the disassembly line balancing in the planning recovery network. This study develops a mixed integer non-linear programming problem for the same. The model is validated using various products from the liquid crystal displays industry.

Innovative supply chain optimization models with multiple uncertainty factors

Abstract: Uncertainty is an inherent factor that affects all dimensions of supply chain activities. In today’s business environment, initiatives to deal with one specific type of uncertainty might not be effective since other types of uncertainty factors and disruptions may be present. These factors relate to supply chain competition and coordination. Thus, to achieve a more efficient and effective supply chain requires the deployment of innovative optimization models and novel methods. This preface provides a concise review of critical research issues regarding innovative supply chain optimization models with multiple uncertainty factors. It also introduces the special issue’s research papers and their respective insights.