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This paper investigates the impact of import competition on energy efficiency among firms in China. Using a difference-in-differences approach that exploits industry- and firm-level tariff reductions following China's accession to the WTO, we examine the effects of reduced output tariffs on coal intensity, measured as coal use per output, of Chinese manufacturing firms. Our findings indicate that higher levels of import competition lead to significant reductions in coal intensity, without negatively affecting firm production levels. We also find evidence that these effects are primarily driven by multi-product firms adjusting their product mix to prioritize energy-saving options. [ABSTRACT FROM AUTHOR]

Product changeovers are especially critical for multiproduct environments where key production system requirements are flexibility, time, and quality. Massive waste of production time and environmental pollution increase with changeovers significantly. It is noted that the green supply chain is gradually emerging, and the environmental policies in countries are also increasing pressure on manufacturers globally. However, how to improve the changeovers' environmental performance in manufacturing enterprise are not entirely focused. The present study aims to raise the changeovers' time efficiency and reduce environmental pollution in the multiproduct production system. This paper first analyzes the characteristics of multiproduct production systems and the causes of inefficient work and pollution and then extracts the problems that need to be optimized. They are the frequent changeover work, complex operation programs, and load imbalance. Multiproduct Production Fast Changeover (MPFC) is developed based on these problems, which integrates the Analytic Hierarchy Process, Entropy Weight method, Divisive Analysis, and Firefly algorithm. In addition, Divisive Analysis's distance calculation is improved for flexible clustering targets. Firefly algorithm's exploration, exploitation, and population coordination mechanisms have also been enhanced. The effectiveness of MPFC is proved in a real multiple flow-lines production case: time efficiency was increased, while the multiple industrial pollutions and key resource consumption were also reduced. [ABSTRACT FROM AUTHOR]

Multiproduct firms often diversify into technologically related activities to exploit efficiencies of joint production; however, unrelated products in the company's portfolio provide access to distinct markets and can help to avoid industry-specific shocks. Yet, the underlying mechanisms of related and unrelated diversification are still poorly understood. Here we investigate diversification decisions of firms in periods when corporations' markets are hit by a demand shocks. In these times, cost efficiency considerations might drive firms to reduce costs by narrowing product portfolios and focusing on combinations of technologically related products, in which economies of scope and mutual capabilities can be exploited. To test this hypothesis, we consider two measures of demand shocks, decreasing sales volumes on the product market and increasing import competition; and analyze their association with changes of product portfolios of Hungarian firms in the 2003-2012 period. We find that production has become more coherent in terms of technological relatedness after firms were exposed to demand shocks. Evidence suggests related adjustment of firm production after demand shocks such that products unrelated to firms' core product are dropped from the portfolio but related products are added. [ABSTRACT FROM AUTHOR]

This paper studies competitive bundling in an oligopoly market with one multi-product dominant firm and several symmetric small firms. In the model of (competing against) specialists each small firm produces a single product, while in the model of (competing against) generalists each small firm produces multiple products. In the model of specialists, we show that the dominant firm will bundle if and only if its dominance level is relatively high. In the model of generalists, the dominant firm's incentive to bundle is stronger than small firms'. In particular, we find that (i) when the dominance level is low enough (and the number of firms is not too large), bundling hurts all firms and no firm bundles; (ii) when the dominance level is relatively high, the dominant firm bundles while small firms sell separately. We also study the impacts of bundling on welfare and derive implications for anti-trust policies. [ABSTRACT FROM AUTHOR]

• We study competition when firms make different compatibility decisions. • The literature only compares full compatibility with full incompatibility. • We learn each firm's incentive about compatibility rather than collective incentives. • Among symmetric firms, compatibility weakly dominates incompatibility for each firm. • Vertical differentiation generates individual incentives for incompatibility. This paper examines the issue of product compatibility in an oligopoly with three multi-product firms. Whereas most of the existing literature focuses on the extreme cases of full compatibility or full incompatibility, we look at asymmetric settings in which some firms make their products compatible with a standard technology and others do not. Our analysis reveals each firm's individual incentive to adopt the standard, and allows to study a two-stage game in which first each firm chooses its technological regime (compatibility or incompatibility), then price competition occurs given the regime each firm has selected at stage one. When firms are ex ante symmetric, we find that for each firm compatibility weakly dominates incompatibility. In a setting in which a firm's products have higher quality than its rivals' products, individual incentives to make products incompatible emerge, first for the firm with higher quality products, then also for the other firms, as the quality difference increases. This paper sheds lights on markets in which some firms adopt the standard technology but other firms use proprietary systems. [ABSTRACT FROM AUTHOR]

This study presents the competition beyond a single product market where a multi-product firm competes with monoproduct rivals through the use of different bundling strategies that impact the firms' incentives to make quality improvements in different ways. The pure bundling strategy can make the multiproduct firm invest in quality improvements when the associated costs are comparatively low, and the additional utility of quality improvement is relatively high, but it discourages single-product firms from improving quality. In the case of mixed bundling, this result inevitably happens in the most competitive market, and is probably found in the less competitive market when markets are not so different in competitive intensity. Therefore, both bundling strategies affect the consumer surplus when the two markets are significantly different in the intensity of competition due to the negative influence of the market distortions after interrelating the two markets. [ABSTRACT FROM AUTHOR]

• Investigate if exports of core and non-core products are interconnected. • Find new evidence that non-core products complement core exports. • The complementarity is only one-way (within a firm-destination pair). • Indications of demand-side rather than supply-side explanations. • Ignoring the one-way complementarity under-emphasizes the core and over-emphasizes individual trade flows. Export sales of multi-product firms are known to be skewed towards their best performing core products. Less is known about the large number of products outside the core that still account for a considerable share of the export value. I use high-quality Swedish firm-registry data to investigate if the exports of core and non-core products are systematically interconnected. Using a novel instrumental variable approach, I find evidence that the exports of non-core products respond to trade of the core. Conversely, the same complementarity is not found using non-core products as placebo-cores. Decomposing the response, I find that over a quarter of the effect can be attributed to price changes of non-core products, suggestive of demand-side explanations. The main contribution of this paper is identifying a new, sizeable, and systematic within-firm one-way complementarity between products that can explain non-core product trade flows. Ignoring this pattern of cross-product dependence may lead to an under-emphasis on the core and over-emphasis on individual trade flows as products should not be viewed in isolation, since the 'superstar' core has (contemporaneous) followers. [ABSTRACT FROM AUTHOR]

With the growing availability of high-quality higher-dimension data in international trade, many new stylized facts have also emerged. One such stylized fact is that multiproduct firms play a significant role in international trade. In this paper, we investigate the effect of US antidumping duties on the exports of Indian multiproduct firms. In particular, we study whether US antidumping duties lead the Indian exporter to alter their product-scope to third country markets (aka to trade partners other than the US). Using a unique transaction-level data from India, we find that firms affected by US antidumping duties increased the number of products exported to other destinations by about 0.7 products, on average. This translates to a substantial 40% increase in the product-scope of these firms because a typical Indian exporting firm exported an average of 1.8 products to a given destination in our sample. We also find that firms whose products spanned multiple sectors drove most of this increase. However, we do not find any difference in the product-scope response of firms producing differentiated vs. those producing homogenous products. We find our results to be robust across various specification and sample size changes. [ABSTRACT FROM AUTHOR]

Highlights • An integrated multiproduct, multi-buyer supply chain under penalty, green, and quality control policies and a vendor-managed inventory with consignment stock agreement. • Optimal batch-sizing policy in an integrated multiproduct constrained supply chain. • Use of the outer approximation with equality relaxation and augmented penalty algorithm for optimal batch-sizing. • New integer variables and real stochastic constraints. • Numerical results and managerial implications verify the performance of the model and the algorithm. Abstract It is of great importance to develop an optimal supply chain (SC) batch-sizing policy that collectively embodies green policies and a vendor-managed inventory (VMI) with consignment stock (CS) agreement. This article provides a mathematical model that includes the buyers' total cost (TC) and the vendor's TC in an SC under penalty, green, and quality control (QC) policies and a VMI-CS agreement. The proposed model is a multiproduct, multi-buyer model and has real stochastic constraints. Moreover, the model differentiates between the holding costs for financial and nonfinancial components, in which the first includes the investment in the space and the second includes the cost due to physical storage, movement, and insurance of the products. Financial components are carried by the vendor on implementation of the VMI-CS agreement, while holding costs for stocking items in the buyers' warehouses are carried by the buyers as nonfinancial components. The objective is to determine the optimal batch-sizing policy with the minimum TC in the integrated SC that finds both the number of the vendor's batches for each of the transported products and the volume of the batches transported to the buyers so as to minimize the TC of the integrated SC while the stochastic constraints are satisfied. Because of the complexity of the optimization model and mathematical formulations, an outer approximation with equality relaxation and augmented penalty algorithm is presented to determine the optimal batch-sizing policy. With application of this technique, the large-scale and hard-to-solve mixed-integer nonlinear programming problem is minimized. The optimality criteria results obtained in numerical examples and sensitivity analysis demonstrate the excellent performance of the method used. Finally, managerial insights, analytical results, and future research directions are provided. [ABSTRACT FROM AUTHOR]

Highlights • How to optimize batch schedules of a multiproduct pipeline network is addressed. • An MILP formulation is proposed to seek the minimum makespan. • Operation constraints of pipelines and stations are taken into consideration. • The proposed model is tested on two in-service multiproduct pipeline networks. Abstract This paper addresses how to optimize detailed schedules of a multiproduct pipeline network. A continuous-time mixed integer linear programming (MILP) formulation is developed to seek the minimum makespan of transportation tasks. Operation constraints about sequence of products, size of single-product batches, flow rate of delivery and injection operations, pumping rate of pipeline segments, batch tracking, conditions of implementing injection or delivery operations, inventory management of stations and consumer markets' demand are all satisfied. The MILP formulation is illustrated by three pipeline networks. Results show the proposed MILP model has a better performance than previous works. [ABSTRACT FROM AUTHOR]

Highlights • MILP-based approaches for supply chain networks under demand uncertainty are proposed. • Planning and scheduling of multiproduct multistage continuous plants are considered. • HMPC and Local Search algorithms are combined to solve a motivative problem. • Solutions are compared with Cutting Plane approach and MPC from the literature Abstract This work presents efficient MILP-based approaches for the planning and scheduling of multiproduct multistage continuous plants with sequence-dependent changeovers in a supply chain network under demand uncertainty and price elasticity of demand. This problem considers multiproduct plants, where several products must be produced and delivered to supply the distribution centres (DCs), while DCs are in charge of storing and delivering these products to the final markets to be sold. A hybrid discrete/continuous model is proposed for this problem by using the ideas of the Travelling Salesman Problem (TSP) and global precedence representation. In order to deal with the uncertainty, we proposed a Hierarchical Model Predictive Control (HMPC) approach for this particular problem. Despite of its efficiency, the final solution reported still could be far from the global optimum. Due to this, Local Search (LS) algorithms are developed to improve the solution of HMPC by rescheduling successive products in the current schedule. The effectiveness of the proposed solution techniques is demonstrated by solving a large-scale instance and comparing the solution with the original MPC and a classic Cutting Plane approach adapted for this work. [ABSTRACT FROM AUTHOR]

This paper addresses the operation of a unidirectional multiproduct pipeline system integrated with inventory tanks management at the refinery and distribution center. A Mixed Integer Linear Programming (MILP) formulation is proposed to model the scheduling of a pipeline operation consisting of a single source node and a single destination node representing a refinery and a distribution center, respectively. Different operational aspects such as product availability, inventory limits, product quality, forbidden sequences, transition between different products injected into the pipeline and tanks inventory management are considered. Time synchronization among refinery, pipeline, and distribution center is guaranteed enhancing the model representation of real operations. Additionally, a solution procedure is developed to improve the model solution performance that explores the reduction of the number of variables and constraints. Different examples based on a real world case are solved showing the model's applicability, which proves to be effective in generating feasible solutions in reasonable computational times. [ABSTRACT FROM AUTHOR]

This work proposes a novel approach for the optimal design of multiproduct supply chain networks (SCN). Through a mixed integer linear programming (MILP) formulation the aim is to establish the structure of facilities that minimizes costs over the planning horizon, taking into account all the SCN's distinctive characteristics. We develop a generalized approach optimally determining the location of various types of facilities, multiproduct flows and demand fulfillment from any node in the network. This allows capturing the intertwined nature of decisions, leading to more efficient results. The proposed approach does not limit the number of echelons or layers. Instead, through a novel formulation, the optimal number of echelons is determined by the model, depending on the product to be sup-plied. To capture the economies of scale governing capital investments and operational costs, different types of facilities are proposed. Besides, the transportation expenses take different unitary costs according to the type of nodes being connected. Finally, the concept of waiting cost is introduced in order to capture the responsiveness of the SCN through the measurement of the time required to fulfill the clients' demands. A case study with different demand patterns and data structures is addressed to assess the potentials and efficiency of the SCN designs obtained with the proposed approach. [ABSTRACT FROM AUTHOR]

The ability to produce multiple types of products using the same manufacturing system plays an essential role in the success of a manufacturing enterprise. Meanwhile, most complex manufacturing systems include many stages, called multistage manufacturing systems (MMSs). MMS has a cascade property, which means the product quality is not only affected by the current stage, but is also related to the outputs of the upstream stage. Multiproduct MMSs ( ${{{{{{\text{M}}}}}}}^{ 3}$ Ss) have been widely applied in industry. Thus, this paper is devoted to modeling and analyzing steady-state ${{{{{{\text{M}}}}}}}^{ 3}$ Ss for quality improvement. The discrete Markov model for single-product-multistage system is extended to novel Markov models for multiproduct-two-stage systems and multiproduct-multistage systems by calculating state transition probabilities of six key manufacturing parameters to obtain an acceptable product quality probability. Based on the developed models, product sequence analysis is carried out to obtain the best product sequence under Bernoulli conditions and Bernoulli relaxation conditions, and bottleneck analysis under Bernoulli conditions is conducted to identify the machine and/or parameters whose improvement can lead to the largest quality improvement. The applicability of the proposed models is validated through numerical experiments and a case study using real-world data. [ABSTRACT FROM AUTHOR]

In a competitive environment, supply chains are competing with each other to gain the market share and competition is a critical factor influencing the supply chain network structure. The current paper presents a variational inequality formulation and provides the results for a competitive supply chain network design model. The new-entrant supply chain competes against an existing one in a non-cooperative behavior. The networks include raw material suppliers, manufacturers, retailers, and the same demand markets. The manufacturers produce multiple products with deterministic, price-dependent demand. The goal is to maximize the future revenue of both chains. The problem is modeled by mathematical programming and the governing Nash equilibrium conditions are derived. Then, a finite-dimensional variational inequality formulation is presented to solve the equilibrium problem. Qualitative properties of the equilibrium pattern are provided to establish existence and uniqueness results under reasonable conditions. The modified projection algorithm is used to solve the variational inequality problem. A numerical example is presented in order to show the efficiency of the proposed model and to investigate the behavior of the model under different conditions. [ABSTRACT FROM AUTHOR]

We present optimization formulations for multi-product supply chain networks. The formulations use a general graph representation that captures dependencies between an arbitrary number of products, technologies, and transportation paths. We discuss how to use the framework to compute compromise solutions that resolve geographical and stakeholder conflicts. We present case studies in which we seek to design supply chains to collect and process organic waste from a large number of farms in the State of Wisconsin to mitigate point phosphorus and methane emissions. [ABSTRACT FROM AUTHOR]

We study the multiproduct price optimization problem under the multilevel nested logit model, which includes the multinomial logit and the two-level nested logit models as special cases. When the price sensitivities are identical within each primary nest, that is, within each nest at level 1, we prove that the profit function is concave with respect to the market share variables. We proceed to show that the markup, defined as price minus cost, is constant across products within each primary nest, and that the adjusted markup, defined as price minus cost minus the reciprocal of the product between the scale parameter of the root nest and the price-sensitivity parameter of the primary nest, is constant across primary nests at optimality. This allows us to reduce the multidimensional pricing problem to an equivalent single-variable maximization problem involving a unimodal function. Based on these findings, we investigate the oligopolistic game and characterize the Nash equilibrium. We also develop a dimension reduction technique which can simplify price optimization problems with flexible price-sensitivity structures. [ABSTRACT FROM AUTHOR]

This study explores the effect of delayed differentiation on a multiproduct vendor-buyer integrated inventory system with rework to identify its potential benefits and provide managers with in-depth information for operational decision-making. The main considerations of the proposed study include a multiproduct fabrication plan to increase machine utilization, a rework process to ensure product quality, and a multi-shipment policy to distribute the end products. In addition, these products sharing an intermediate part for which a two-stage fabrication scheme is adopted, wherein the common parts are produced at the first stage and the end products are manufactured at the second stage. The aim is to reduce the overall system costs and shorten the replenishment cycle time. Mathematical modeling and optimization methods were employed to derive the closed-form optimal replenishment cycle time and delivery decisions. We demonstrated the applicability of our research results through numerical examples and revealed that for both linear and nonlinear relationships between the common intermediate part's completion rate α and its practical value at α, our proposed two-stage production scheme with delayed differentiation is considerably beneficial vis-à-vis single-stage schemes in saving overall system costs and reducing the replenishment cycle time. [ABSTRACT FROM AUTHOR]

Optimizing the scheduling of liquid drug product manufacturing is paramount for pharmaceutical companies in their increasingly competitive environment and requires the modelling of industry-specific constraints. Such constraints include: (i) changing sequence-dependent setup times; (ii) maintaining a sterile production environment (e.g., through sterile holding times); (iii) periods with limited or no plant activity (e.g., no workforce during weekends); and (iv) demand timing (i.e., delivery deadline and release date constraints). In this work, an immediate precedence model is formulated to optimize the scheduling of liquid drug product manufacturing, considering the industry-specific constraints. The primary objective is to minimize the production makespan. Four case studies comprising up to 38 batches from a real multi-product facility illustrate the performance of the rigorous optimization approach. The makespan could be reduced by up to 7.9% compared to expert schedules. [ABSTRACT FROM AUTHOR]

We address the problem of production scheduling in multi-product multi-stage batch plants. Unlike most of the previous works, which propose continuous-time models, we study discrete-time mixed-integer programming models and solution methods. Specifically, we discuss two models based on network representations of the facility and develop two new models inspired by the Resource-Constrained Project Scheduling Problem. Furthermore, we propose different solution methods, including tightening methods based on processing unit availability, a reformulation based on processing unit occupancy, and an algorithm to refine approximate solutions for large-scale instances. Finally, we present a comprehensive computational study which shows that speedups of up to four orders of magnitude in are observed when our models and methods are compared to existing approaches. [ABSTRACT FROM AUTHOR]

This contribution introduces an efficient constraint programming (CP) model that copes with large-scale scheduling problems in multiproduct multistage batch plants. It addresses several features found in industrial environments, such as topology constraints, forbidden product-equipment assignments, sequence-dependent changeover tasks, dissimilar parallel units at each stage, limiting renewable resources and multiple-batch orders, among other relevant plant characteristics. Moreover, the contribution deals with various inter-stage storage and operational policies. In addition, multiple-batch orders can be handled by defining a campaign operating mode, and lower and upper bounds on the number of batches per campaign can be fixed. The proposed model has been extensively tested by means of several case studies having various problem sizes and characteristics. The results have shown that the model can efficiently solve medium and large-scale problems with multiple constraining features. The approach has also rendered good quality solutions for problems that consider multiple-batch orders under a campaign-based operational policy. [ABSTRACT FROM AUTHOR]

The theory of virtual quantities, the dual to virtual prices, provides a framework to analyze competitive multiproduct firm behavior under multiple quantity controls on inputs and outputs, including command-and-control quotas and transferable property rights. The framework addresses the firm׳s reactions to regulatory controls, impacts of adding or dropping quantity controls, inferring unrationed from rationed production, and conversion from command-and-control quotas to cap-and-trade systems with transferable property rights and secondary market behavior. The paper develops reasons for failure of quasi-concavity of technology, extends the elasticity of intensity׳s properties, and integrates the virtual price and virtual quantity frameworks. Virtual quantities are applied to assess potential firm responses to quantity controls and a potential transferable property right in a Malaysian fishery. [ABSTRACT FROM AUTHOR]

We model and measure the economic response to taxes on sugar-sweetened beverages (SSB). We hypothesize that when the tax does not cover the geographic extent of the market, cross-border shopping by consumers reduces the pass-through rate of the tax and the effect of the tax on consumption. We further hypothesize that the multi-product nature of retail groceries compounds this competitive effect. We use retail scanner data and a difference-in-difference estimator to quantify effects of the Philadelphia Beverage Tax (PBT) implemented in 2017. We estimate a pass-through rate of 80 percent and a 32-percent reduction in SSB sales caused by the PBT. We find the tax increased sales and revenue of SSBs and of non-taxed products in stores close to Philadelphia but not subject to the tax. In addition, we find lower pass-through rates in stores offering a wider assortment of products. We take this as evidence that multiproduct competition reduces the effect of Philadelphia's SSB tax. An important implication is that a tax implemented at the state or national level would be more effective at reducing SSB consumption. • We use economic theory and empirics to study taxes on sugar-sweetened beverages. • Cross-border competition between food retailers reduces effectiveness of an SSB tax. • Multiproduct nature of food retailers exacerbates the cross-border competition effect. • The Philadelphia Beverage Tax (PBT) increased sales of SSBs in neighboring counties. • The PBT also increased sales of un-taxed products in neighboring counties. • In Philadelphia, tax passthrough was larger in stores with smaller product assortment. [ABSTRACT FROM AUTHOR]

In a turbulent and highly competitive business environment, management always pursues options to reduce overall operating costs. The vendor-buyer integrated system has recently drawn attention from managers, because it can benefit both parties of the supply chain and it is suitable to be applied to a so-called intra-supply chain system within the present-day globalized enterprise. This study attempts to simultaneously determine production and shipment decisions for a multi-product vendor-buyer integrated inventory system with a rework process, wherein multiple products are fabricated in sequence by a single machine under a rotation cycle time policy. All defective items produced in regular production are assumed repairable, and are reworked right after the regular production ends. Finished goods of each product are transported to sales offices/customers after rework. A multi-delivery policy is applied, wherein a fixed quantity of n instalments of the finished batch is delivered at fixed intervals during the delivery timeframe. Mathematical modelling and optimization techniques are used to help simultaneously determine the optimal production and shipment decisions that minimize the expected overall system costs. A numerical example is used to show the applicability of our research results. [ABSTRACT FROM AUTHOR]

Continuous multi-product plants allow the production of several products (product grades). During grade transitions off-spec products are produced. The economic losses and the environmental impact of these transitions are sequence dependent, so the amount of off-grade products can be minimized by scheduling the sequence of the production of different products. Applying parallel production sites increases the flexibility of multi-product plants. Since market demands are changing, the production cycles of these sites should be re-scheduled in certain intervals. Therefore, our task is to design production cycles that contains all required products by minimizing the total length of grade transitions. Most production scheduling problems such as the one considered in this paper are NP-hard. Our goal is to solve realistic problem instances in no more than a couple of minutes. We show that this problem can be considered as a multiple traveling salesmen problem (mTSP), where the distances between the products are based on the time or costs of the grade transitions. The resulted mTSP has been solved by multi-chromosome based genetic algorithm. The proposed algorithm was implemented in MATLAB and is available at the website of the authors (Abonyi). For demonstration purposes, we present an illustrative example. The results show that multi-product multi-site scheduling problems can be effectively handled as mTSPs, and the proposed problem-specific representation based genetic algorithm can be used in wide range of optimization problems. [ABSTRACT FROM AUTHOR]

We present an effective scheduling heuristic for realistic production planning in a petrochemical blending plant. The considered model takes into account orders spanning a multi-product portfolio with multiple bills of materials per product, that need to be scheduled on shared production facilities including a complex pipeline network. Capacity constraints, intermediate storage restrictions, due dates, and the dedication of resources to specific product families have to be respected. The primary objective of the heuristic is to minimize the total order tardiness. Secondary objectives include the minimization of pipeline cleaning operations, the minimization of lead times, and the balanced utilization of filling units. The developed algorithm is based on a dynamic prioritization-based greedy search that schedules the orders sequentially. The proposed method can schedule short to mid-term operations and evaluate different plant configurations or production policies on a tactical level. We demonstrate its performance on various real-world inspired scenarios for different scheduling strategies. Our heuristic was used during the construction phase of a new blending plant and was instrumental in the optimal design of the plant. [ABSTRACT FROM AUTHOR]

In this article we present a new approach, relying on mixed-integer linear programming (MILP) formulations, for the design of multi-product batch plants with continuous sizes for processing units and host selection. The main advantage of the proposed approach is its scalability, that allows us to solve, within reasonable precision requirements, realistic instances. Furthermore, we show that many other alternatives are either numerically unstable (for the problem sizes that we are interested in), unable to solve large instances, or much slower than the proposed method. We present extensive computational experiments, which show that we are able to solve almost all tested instances, and, in average, we are ten times faster than alternative approaches. As we use a high level implementation language (AMPL) we should get further time improvements if lower level implementations are used (C, C ++ ). Reproducibility of our results can be tested using our models and data available on-line at BPLIB. 1 1 Available at: http://www.dii.uchile.cl/~daespino/ [ABSTRACT FROM AUTHOR]

Recently, the first oligopolistic competition model of the closed-loop supply chain network involving uncertain demand and return has been established. This model belongs to the context of oligopolistic firms that compete noncooperatively in a Cournot-Nash framework. In this paper, we modify the above model in two different directions. (i) For each returned product from demand market to firm in the reverse logistics, we calculate the percentage of its optimal product flows in each individual path connecting the demand market to the firm. This modification provides the optimal product flow routings for each product in the supply chain and increases the optimal profit of each firm at the Cournot-Nash equilibrium. (ii) Our model extends the method of finding the Cournot-Nash equilibrium involving smooth objective functions to problems involving nondifferentiable objective functions. This modification caters for more real-life applications as a lot of supply chain problems involve nonsmooth functions. Existence of the Cournot-Nash equilibrium is established without the assumption of differentiability of the given functions. Intelligent algorithms, such as the particle swarm optimization algorithm and the genetic algorithm, are applied to find the Cournot-Nash equilibrium for such nonsmooth problems. Numerical examples are solved to illustrate the efficiency of these algorithms. [ABSTRACT FROM AUTHOR]

This paper tackles the challenges for a production planning problem with linguistic preference on the objectives in an uncertain multiproduct multistage manufacturing environment. The uncertain sources are modelled by fuzzy sets and involve those induced by both the epistemic factors of process and external factors from customers and suppliers. A fuzzy multiobjective mixed integer programming model with different objective priorities is proposed to address the problem which attempts to simultaneously minimize the relevant operations cost and maximize the average safety stock holding level and the average service level. The epistemic and external uncertainty is simultaneously considered and formulated as flexible constraints. By defining the priority levels, a two-phase fuzzy optimization approach is used to manage the preference extent and convert the original model into an auxiliary crisp one. Then a novel interactive solution approach is proposed to solve this problem. An industrial case originating from a steel rolling plant is applied to implement the proposed approach. The numerical results demonstrate the efficiency and feasibility to handle the linguistic preference and provide a compromised solution in an uncertain environment. [ABSTRACT FROM AUTHOR]

Product mix influences the performance of pull production control strategy in multi-product manufacturing systems. The complexity of product mix on the performance of a manufacturing system is primarily based on the characteristics of the demand and production control strategies. Demands are mainly characterised by volume and product-type while production control strategy is characterised by material release time, part flow, inventory control and throughput times. In multi-product systems, pull production control strategy operates dedicated or shared Kanban allocation policy. This paper examines the performance of the Generalised Kanban Control Strategy (GKCS), Extended Kanban Control Strategy (EKCS) and Basestock Kanban-CONWIP (BK-CONWIP) control strategy operating Shared Kanban Allocation Policies (S-KAP) or Dedicated Kanban Allocation Policies (D-KAP) for a healthcare parallel/serial assembly line with setup times. A simulation based multi-objective optimisation technique was adopted to examine the effect of different product mixes on the strategies and policies. A ranking and selection technique for multiple systems was used to screen the performance of the strategies. It was shown that product mix variability in a system influence the inventory levels of the pull control strategies examined. However, the performances of the strategies vary with strategies operating S-KAP having better inventory control than strategies operating D-KAP. Similarly, BK-CONWIP outperformed its alternatives. [ABSTRACT FROM AUTHOR]

In this paper, a multiproduct single vendor–single buyer supply chain problem is investigated based on the economic production quantity model developed for the buyer to minimize the inventory cost. The model to be more applicable for real-world supply chain problems contains five stochastic constraints including backordering cost, space, ordering, procurement, and available budget. The objective is to find the optimal order quantities of the products such that the total inventory cost is minimized while the constraints are satisfied. The recently-developed sequential quadratic programming (SQP), as one of the best optimization methods available in the literature, is used to solve the problem. Twenty numerical examples in 3 scales of small, medium, and large are solved in order to demonstrate the applicability of the proposed methodology and to evaluate its optimum performance. The results show that SQP has satisfactory performance in terms of optimum solutions, number of iterations to achieve the optimum solution, infeasibility, optimality error, and complementarity. Besides, the optimum performance of the SQP method is compared with the one of another exact method called interior point using the above numerical examples under similar conditions. The comparison results are in favor of the employed SQP. At the end, a sensitivity analysis is performed on the change rate of the objective function obtained based on the change rate of the variance of the order quantity. [ABSTRACT FROM AUTHOR]

This paper develops an analytical method to measure the performance of a multi-product unreliable production line with finite buffers between workstations. The performance measure used in this paper is Total Cycle Time. The proposed method is an approximation that generalizes the processing times to ease the variation of product types in a multi-product system. A decomposition method is then employed to approximate the production rate of a multi-product production line. The decomposition method considers generally distributed processing times as well as random failure and repair. A GI/G/1/N queuing model is also applied to obtain parameters such as blocking and starvation probabilities that are needed for the approximation procedure. Several numerical experiments under different scenarios are performed and results are validated by simulation models in order to assess the accuracy and strength of the proposed method. Consequent analysis and discussion of the results are also presented. [ABSTRACT FROM AUTHOR]

This paper attempts to design a reverse supply chain network (SCN), add it to an existing multi-product forward SCN and simultaneously redesign the existing forward supply chain (SC). The problem considers uncertainty on products demand and and also returned products in multi-period context. Benders’ decomposition is applied to solve the stochastic mixed-integer model to optimality. The scenarios are generated based on the demand distribution function using Cholesky’s factorization method to consider correlation among different products’ demands. To decrease the computational effort, the number of scenarios is reduced using k-means clustering algorithm. The method is tested on a cell phone SC. [ABSTRACT FROM AUTHOR]

This paper studies a practical multi-factory job allocation and scheduling problem involving inland and maritime transport limits. A new heuristic called Due-date Based Cut-off rule (DBC) is developed to improve the computational efficiency of both exact and genetic algorithms (GA). Except the application of DBC, this proposed GA is guided by a novel fuzzy controller aimed at eliminating the drawbacks other GAs have when dealing with multi-factory models. The tests of the solution quality and computational efficiency for this GA are carried out. The numerical experiments demonstrate the value of the proposed approach in this practical global supply chain. [ABSTRACT FROM AUTHOR]

This study presents an integrated simulation and genetic algorithm (GA) for optimum operator allocation in a large multi product assembly shop. At first, simulation is used as an exquisite tool for modeling and analyzing the true performance of the system. Then, GA is used to maximize throughput of the system. In other words, optimal number of operators is found using GA such that the throughput is maximized. It is shown that the integrated GA-simulation approach yields considerable savings and benefits. The focus of the GA-simulation approach is on complex problem settings where there is random stochastic variability in the modeling environment. The results of this study show that the integrated GA-simulation is ideal for problems with several numbers of parameters and variables, and complex objective function. This is the first study that integrates GA and simulation for optimum allocation of operators in multi product assembly shops. [ABSTRACT FROM AUTHOR]

As market demand forecasts will become more uncertain in the future it is necessary to develop new methods for plant design. New technologies allow combining the flexibility of a batch plant with the efficiency of a continuous production. To gain even more economic benefit intensified equipment is often designed as a module. The usage of equipment modules allows an easily and efficient increase of capacity. In this way a plant expansion close to the market is possible. Consideration of stepwise plant expansion is often limited to the copying of complete production lines. This approach results in high additional investment costs due to the loss of economy of scale. To overcome the limitations set by economy of scale, an equipment wise expansion strategy should be applied. By debottlenecking the capacity limits of the plant it is possible to adapt plant capacity very close to the changing environment and reduce the additional costs. Therefore, design of a modular plant must be combined with suitable economic evaluation methods for uncertain demand forecasts. In this work a framework for such a design based on predefined and standardized modules will be presented. The framework consists of two stages. The first stage is the selection of possible modular setups and suitable expansion strategies. Next these setups are evaluated in a real option analysis to investigate the economic performance in an uncertain market. The approach will be used to evaluate a multiproduct continuous plant. The main finding is that the economy of the modular plant depends under the given boundary conditions on the equipment item with the highest proportion on total investment costs and a high cost degression exponent. [ABSTRACT FROM AUTHOR]

The procedure for synthesizing optimal multiproduct reactor systems is proposed. At the first stage, the theoretically possible values of selectivities with respect to products are determined for the specified market demand for the products. The capacity of processing the initial reagent is specified. At the second stage, the engineering, operating, and structural parameters of the unit that provides the values of selectivities, which are closest to the theoretical parameters, are determined. [ABSTRACT FROM AUTHOR]

An inventory control system has been developed for a distribution system consisting of a single multiproduct warehouse serving a set of customers and purchasing products from multiple vendors. Purchase orders requesting multiple products are delivered to the warehouse in a process referred to as joint replenishment. The receipt of customer orders by the warehouse proceeds in order intervals and in order quantities that are subject to random fluctuations. The objective of warehouse operation is to minimize the total cost while maintaining inventory levels within the warehouse capacity by adjusting the purchase order intervals and quantities. An adaptive model predictive control algorithm is developed using a periodic square wave model to represent the material flows. The adaptive concept incorporates a stabilized minimum variance control-type input calculation coupled with input/output stream parameter predictions. The boundedness of the control output under the suggested algorithm is mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of the scheme was demonstrated using simulations. [ABSTRACT FROM AUTHOR]

This paper is devoted to modeling and analysis of multiproduct manufacturing systems with homogeneous exponential machines and finite buffers. In such systems, each machine processes multiple product types with different speeds, but the processing time for each product type is the same on all machines. Buffers are finite, shared for all products. Analytical methods to evaluate the system performance are developed, and system-theoretic properties are investigated. It is shown from numerical experiments that such a method has a high accuracy in performance evaluation. To improve the performance of such systems, bottleneck analysis is carried out to identify the machine and/or product whose improvement will lead to the largest improvement in system throughput. [ABSTRACT FROM AUTHOR]

Here we discuss a multi-product lot-sizing problem for a job shop controlled with a heijunka box. Demand is considered as a random variable with constant variation which must be absorbed somehow by the manufacturing system, either by increased inventory or by flexibility in the production. When a heijunka concept (production leveling) is used, fluctuations in customer orders are not transferred directly to the manufacturing system allowing for a smoother production and better production capacity utilization. The problem rather is to determine a tradeoff between the variability in the production line capacity requirement and the inventory level. [ABSTRACT FROM AUTHOR]

This study concerns the coordination of pricing and inventory decisions in a multiproduct two-stage supply chain that consists of one manufacturer and multiple retailers within a competitive environment. The retailers order some substitutable products from a common manufacturer. It is assumed that channel members have different market power. The purpose of this paper is to coordinate pricing and inventory decisions such that utility of all involved levels (manufacturer and retailers) is met. Hence, a nonlinear multidivisional bi-level programming model is developed. This model considers both retailers and manufacturer when deciding about the pricing and production volume (for manufacturer) or amount of purchase (for retailers). A hybrid of genetic algorithm (GA) and local search method is proposed to solve the nonlinear bi-level model. This model is reduced to a nonlinear programming by replacing the Karush-Kuhn-Tucker (KKT) conditions of followers to the lower level of the model. Then, the obtained single-level model is relaxed to a linear model to achieve an upper bound (UB). Finally, a numerical example is presented to analyze which parameters have more effect on the price, lot size and, consequently, on the profit. Results show that increasing the market scale parameter of the manufacturer increases the profit of the manufacturer, but the market scale parameter of retailers has no effect on the manufacturer's profit, although it increases the retailers' profit. [ABSTRACT FROM AUTHOR]

This article models a single-stage hybrid production system, which can be regarded as a Make To Order (MTO) production system with safety stocks or a Make To Stock (MTS) production system with advance demand information. In an environment with multiple products and variable customer due dates, optimality conditions for safety stocks (base stocks) and safety lead times (work-ahead window) that minimize inventory and backorder costs are derived. For a simplifiedM/M/1 system with exponentially distributed customer required lead time, an explicit comparison between MTO and MTS is conducted. A pure MTO policy gets relatively more favorable to a pure MTS policy if inventory holding costs increase, backorder costs decrease, the mean customer required lead time increases, or the processing rate increases. In a numerical study, the influence of variance, the behavior of optimal parameters, and the cost reduction potential of this hybrid policy are shown. [ABSTRACT FROM AUTHOR]

Abstract: In this paper artificial neural network is introduced to forecast the daily electricity consumption of a multiproduct pipeline which is used to drive oil pumps. Forecasting electricity energy consumption is complicated since there are so many parameters affecting the energy consumption. Two different sets of input vectors are selected from these parameters by detailed analysis of energy consumption in this study, and two corresponding multilayer perceptron artificial neural network (MLP ANN) models are developed. To enhance the generalization ability, the numbers of hidden layers and neurons, activation functions and training algorithm of each model are optimized by the trial-and-error process step by step. The performances of the two proposed MLP ANN models are evaluated on real data of a Chinese multiproduct pipeline, and compared with two linear regression and two support vector machine (SVM) models which are produced using different inputs. Results show that the two MLP ANN models have very high accuracy for prediction and better forecasting performance than the other models. The proposed input vectors and MLP ANN models are useful not only in the effective evaluation of batch scheduling and pumping operation, but also in the energy consumption target setting. [Copyright &y& Elsevier]

The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a single-vendor single-buyer supply chain system, in which the vendor is responsible to manage the buyer's inventory. To include an extended applicability in real-world environments, the multiproduct economic production quantity model with backordering under three constraints of storage capacity, number of orders, and available budget is considered. The nonlinear programming model of the problem is first developed to determine the near optimal order quantities along with the maximum backorder levels of the products in a cycle such that the total VMI inventory cost of the system is minimized. Then, a genetic algorithm (GA) based heuristic is proposed to solve the model. Numerical examples are given to both demonstrate the applicability of the proposed methodology and to fine tune the GA parameters. At the end, the performance of the proposed GA is compared to the one of the LINGO software using different problem sizes. The results of the comparison study show that, while the solutions do not differ significantly, the proposed GA reaches near optimum solutions in significantly less amount of CPU time. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a continuous-time approach for scheduling reversible-flow pipelines. [•] The model allows to change the flow direction as many times as needed. [•] We seek to minimize pumping, interface, setup and inventory carrying costs. [•] Input/output schedules at pipeline extremes and intermediate depots are generated. [•] Optimal schedules for real world case studies are found in low CPU times. [ABSTRACT FROM AUTHOR]

Although oligopoly theory is generally concerned with the single-product firm, what is true in the real word is that most of the firms offer multiproducts rather than single products in order to obtain cost-saving advantages, cater for the diversity of consumer tastes, and provide a barrier to entry We develop a dynamical multiproduct Cournot duopoly model in discrete time, where each firm has an owner who delegates the output decision to a manager. The principle of decision-making is bounded rational. And each firm has a nonlinear total cost function due to the multiproduct framework. The Cournot Nash equilibrium and the local stability are investigated. The tangential bifurcation and intermittent chaos are reported by numerical simulations. The results show that high output adjustment speed can lead to output fluctuations which are characterized by phases of low volatility with small output changes and phases of high volatility with large output changes. The intermittent route to chaos of Flip bifurcation and another intermittent route of Flip bifurcation which contains Hopf bifurcation can exist in the system. The study can improve our understanding of intermittent chaos frequently observed in oligopoly economy. [ABSTRACT FROM AUTHOR]

Highlights: [•] First multi-purpose dividing wall column in production scale worldwide. [•] Adaptable and flexible column for multi product use. [•] New simulation approach, overcoming initialization issues. [•] No piloting. [ABSTRACT FROM AUTHOR]

The multiproduct two-layer supply chain is very common in various industries. In this paper, we introduce a possible modeling and algorithms to solve a multiproduct two-layer supply chain network design problem. The decisions involved are the DCs location and capacity design decision and the initial distribution planning decision. First we describe the problem and give a mixed integer programming (MIP) model; such problem is NP-hard and it is not easy to reduce the complexity. Inspired by it, we develop a transformation mechanism of relaxing the fixed cost and adding some virtual nodes and arcs to the original network. Thus, a network flow problem (NFP) corresponding to the original problem has been formulated. Given that we could solve the NFP as a minimal cost flow problem. The solution procedures and network simplex algorithm(INS) are discussed. To verify the effectiveness and efficiency of the model and algorithms, the performance measure experimental has been conducted. The experiments and result showed that comparing with MIP model solved by genetic algorithm (GA) and Benders, decomposition algorithm (BD) the NFP model and INS are also effective and even more efficient for both small-scale and large-scale problems. [ABSTRACT FROM AUTHOR]

We study a fashion retailer's dynamic pricing problem in which consumers present reference effect and memory window. Based on the theory of Baucells et al. (2011), we propose a new reference-price updating mechanism in fashion and textile (FT) industry where consumers have a bounded memory window and anchor on the first and most recent price in any memory window. Moreover, we study the impacts of this mechanism on optimal pricing policy for a retailer selling multiple fashion-like products and analyze optimal price's steady state, monotonicity, and convergence. For two-product case, we find that, for otherwise identical products, the steady-state price of a core product is lower than that of a noncore product. We compute the retailer's loss of revenue if he incorrectly assumes the reference-price effect to be at the product level and prices the products individually. Further, as illustrated with numerical results, our model is a flexible way to make pricing strategy if the retailer can anticipate the length of consumers' memory window. [ABSTRACT FROM AUTHOR]