Tight Capacity Research Articles

Supply Chain Constraints and Inflation

We develop a multisector, open economy, New Keynesian framework to evaluate how potentially binding capacity constraints, and shocks to them, shape inflation. We show that binding constraints for domestic and foreign producers shift domestic and import price Phillips Curves up, similar to reduced-form markup shocks. Further, data on prices and quantities together identify whether constraints bind due to increased demand or reductions in capacity. Applying the model to interpret recent US data, we find that binding constraints explain half of the increase in inflation during 2021-2022. In particular, tight capacity served to amplify the impact of loose monetary policy in 2021, fueling the inflation takeoff.

PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING UNTUK MEMILIH JURUSAN PADA KAMPUS NEGERI

Students in grade twelfth high school must be able to determine their individual student interests to be able to continue further studies at university. But there are still many students who have not been able to determine their specialization even though they have sufficient understanding and knowledge of learning during high school. This study aims to apply simple additive weighting and calculation of the tightness capacity of each specialization major on a public campus in making a Decision Support System in determining student majors in the Angkasa High School environment. By paying attention to the weight and each criterion for the specialization major, then calculate with the tight capacity of the specialization department on each state campus so that the results issued by the calculation have provided decision support for students to determine specialization and state campuses more precisely. The decision support system uses the Kotlin programming language and Android-based NoSQL MongoDB database. The results of the manual calculation of the Simple Additive Weighting and Opportunity method with a decision support system reach 99% according.

Railway car routing optimization based on the coordinated utilization of station‐line capacities

AbstractTo optimize railway car routing based on balanced station‐line capacities, thereby improving railway freight traffic organization efficiency, and reducing transportation costs, a railway car routing optimization model based on the coordinated utilization of station‐line capacities was developed. Based on the theory of the membership function in fuzzy mathematics, the level of railway station‐line coordination based on capacity utilization was presented. The occupancy of a railway line by each car was taken as a binary decision variable. Using a set of deviation constraints, the deviation between the station‐line flow of the railway network and the utilization of a certain level was expressed in the form of decision variables that were then penalized to different degrees in the objective function. The total transportation cost and the penalty cost were taken as the objective function for minimization. Based on the characteristics of the fuzzy membership degree, the relationship between the penalty for capacity deviation and the membership function in the optimization model was analyzed, and the calculation method of the penalty for the capacity deviation considering the membership function was presented. An adaptive large‐scale neighbourhood search (ALNS) algorithm was designed to solve the model, and its effectiveness was verified. An actual 36‐station railway network was used to validate the proposed railway car routing optimization model through comparison with existing models. The optimization results showed that, at the expense of increasing the transportation cost by 1.05%, the coordination penalty cost was reduced by 11.24%, the overall cost was reduced by 3.21%, and most of the situation with 90% utilization rate of stations and lines in the railway network was eliminated. The model proposed in this paper can help reduce the pressure on lines and stations with tight capacity in the network, improve the utilization of virtual lines and stations and coordination of station‐line capacities.

A Dantzig-Wolfe decomposition-based algorithm for capacitated passenger assignment problem with time-varying demand in high-speed railway networks

This paper proposes an algorithm for solving the schedule-based passenger assignment problem, with explicit consideration of continuous time-varying demand and tight capacity constraints for large-scale high-speed railway (HSR) networks. We construct the space–time travel network and formulate the assignment model by destination-based arc flow variables, then design a Dantzig-Wolfe (D-W) decomposition-based algorithm to solve the large-scale model of this problem with less memory requirement and computational complexity. The sub-problem in each iteration can be solved as a single-source (destination) minimum cost flow problem without capacity constraint by the “rooftops” method, which provides an effective technique to tackle both continuous and discrete time-varying demand in the assignment problem. It is further demonstrated that the assignment solutions for discrete time-varying demand can converge to that for continuous time-varying demand with the increase of demand discretization number. Three real case studies with different HSR network scales show that the proposed algorithm is efficient in memory and computational complexity, especially when highly accurate solutions for passenger assignment on large-scale HSR networks are needed.

Causal Adversarial Channels With Feedback Snooping

With the advent of 5G and technologies such as cloud computing, Internet-of-Things (IoT), etc, future communication networks will consist of a large number of heterogeneous devices connected together. A critical aspect will be ensuring that communication is not only fast and reliable, but also resilient to malicious attack. As networks increasingly adopt zero-trust principles in their security frameworks, it is important to consider such attacks from a zero-trust perspective. Motivated by this, we consider the problem of communicating a message reliably across a binary erasure channel (BEC(<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>)) or a binary symmetric channel (BSC(<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>)) against an adversary actively injecting additional erasures or flips at the channel&#x2019;s input. The adversary can corrupt up to a fraction <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> of the transmitted bits and knows the transmission scheme agreed upon by the communicating terminals. Further, he has the capability to causally snoop in on both the transmitter and the receiver in real time, i.e., if <inline-formula> <tex-math notation="LaTeX">${\mathbf {x}}=(x_{1},x_{2},\ldots, x_{n})$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${\mathbf {y}}=(y_{1},y_{2},\ldots, y_{n})$ </tex-math></inline-formula> denote the transmitted and the received codewords respectively, at each time <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, he knows <inline-formula> <tex-math notation="LaTeX">$(x_{1},x_{2},\ldots, x_{k})$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$(y_{1},y_{2},\ldots, y_{k-1})$ </tex-math></inline-formula>. Using his side-information, the adversary is free to employ any attack that respects his constraint on the number of corruptible bits. We prove an information-theoretic tight capacity characterization as a function of <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> for (i) the erasure adversary with a BEC(<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>) and (ii) the bit-flip adversary with a BSC(<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>). A unique feature of our models is the compounding of stochastic and adversarial noise sources. Our analysis reveals the worst-case adversarial attacks for both models and proves the existence of coding schemes that achieve rates equal to the capacity for any adversarial attack. In the case of bit-flips, we show that, interestingly, when <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is below a certain threshold (that depends on <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>), the adversary is no worse than an i.i.d. memory-less noise source.

Pipeline capacity and the dynamics of Alberta crude oil price spreads

AbstractWe investigate the effect of binding pipeline capacity on price spreads between crude oil in Alberta, Canada, and competing crude oils in export markets at the other end of the pipeline. Our goal is to determine whether these price spreads are consistent with capacity‐constrained spatial arbitrage models by estimating Markov switching models of the price spreads for both heavy and light crude oil. Our results for heavy crude oil agree with the theory: we find one regime with both a low mean and a low variance, consistent with sufficient pipeline capacity, and another regime with both a higher mean and a higher variance, consistent with constrained pipeline capacity. The difference in mean spreads between regimes then provides an estimate of the mean shadow price of capacity. Price spreads for light crude oil do not display significant differences in mean spreads across regimes, suggesting that pipeline capacity constraints are less important for this commodity. Comparing our results with pipeline capacity data, we find that tight capacity is not always associated with large price spreads.

Counting matchings via capacity-preserving operators

AbstractThe notion of the capacity of a polynomial was introduced by Gurvits around 2005, originally to give drastically simplified proofs of the van der Waerden lower bound for permanents of doubly stochastic matrices and Schrijver’s inequality for perfect matchings of regular bipartite graphs. Since this seminal work, the notion of capacity has been utilised to bound various combinatorial quantities and to give polynomial-time algorithms to approximate such quantities (e.g. the number of bases of a matroid). These types of results are often proven by giving bounds on how much a particular differential operator can change the capacity of a given polynomial. In this paper, we unify the theory surrounding such capacity-preserving operators by giving tight capacity preservation bounds for all nondegenerate real stability preservers. We then use this theory to give a new proof of a recent result of Csikvári, which settled Friedland’s lower matching conjecture.

Cudrania tricuspidata leaf extracts and its components, chlorogenic acid, kaempferol, and quercetin, increase claudin 1 expression in human keratinocytes, enhancing intercellular tight junction capacity

Dysfunction of tight junctions and their components can cause diverse skin diseases. Here, we investigated the expression of claudin 1, a major tight junction protein, and changes of tight junction capacity upon treatment of the extracts of Cudrania tricuspidata (C. tricuspidata) and its components, chlorogenic acid, kaempferol, and quercetin. The effects of ethanol extracts of C. tricuspidata (EECT) and water extracts of C. tricuspidata (WECT) on the viability of human keratinocyte HaCaT cells were assessed by cell proliferation assay. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was conducted to measure the expression of claudin 1 mRNA. The protein expression of claudin 1 was analyzed by western blot and its tight junctional distribution was observed with immunofluorescence microscopy analysis. The tight junction capacity was analyzed by dispase assay. Upon treatment of WECT to HaCaT cells, the mRNA and protein expressions of claudin 1 were increased. In addition, chlorogenic acid, kaempferol, and quercetin increased claudin 1 protein expression levels in a dose-dependent manner. WECT and these three compounds enhanced the tight junction capacity of HaCaT cells in dispase assay. WECT, and its components, such as chlorogenic acid, kaempferol, and quercetin, upregulates both mRNA and protein expressions of claudin 1, which leads to the enhancement of tight junction capacity. Thus, WECT could be a therapeutic approach for treating tight junction-disrupted conditions such as atopic dermatitis and psoriasis.

A novel hybrid genetic algorithm for the location routing problem with tight capacity constraints

Location routing problem (LRP) is a popular and challenging topic in the field of logistic systems. LRP needs to address the depot location problem and vehicle routing problem at the same time. Till now, different LRP variants have been formulated to better meet realistic requirements. In this study, we focus on the capacitated LRP (CLRP) with tight capacity constraint on both depots and vehicles. To cope with the tight constraints, a hybrid genetic algorithm (HGA) is developed to search not only feasible solution space but also infeasible solution space. The proposed HGA combines the wide exploration capacity of GA, and the fast exploitation capacity of neighbourhood local search. To evolve GA for CLRP, solutions are represented by sets and sequences, and accordingly, a multi-sequence-based crossover is designed for the offspring generation. Moreover, a population management scheme is designed to facilitate GA’s evolution. Experiments are conducted on two benchmark sets and the results show that HGA is quite competitive with existing well-known CLRP algorithms on the classical instances. Furthermore, HGA is able to obtain quite a number of new best solutions on the real-life-like instances with tighter constraints.

Capacity restrictions and supply chain performance: Modelling and analysing load-dependent lead times

Several studies have reported that capacitated supply chains may benefit from an improved dynamic performance as compared to unconstrained ones. This occurs as a consequence of the capacity limit acting as a production smoothing filter. In this research, the relationship between capacity restrictions and the operational performance of supply chains is investigated from a novel perspective, i.e. we assume that the influence of capacity constraints on lead times depends on the supply chain's responsiveness. Under these circumstances, the experiments show that there is a negative effect of capacity constraints on supply chain performance, both in terms of process efficiency (internal) and customer satisfaction (external). Nonetheless, the magnitude of this impact greatly depends on the responsiveness of the firm, market conditions and adopted policies for inventory management. More specifically, the combination of tight capacity restrictions and low responsiveness significantly contributes to decrease supply chain performance, which may be very damaging for the dynamic behavior of the system. In this sense, efficient capacity planning proves to be essential to prevent the supply chain from entering into a vicious circle.

A kernel search to the multi-plant capacitated lot sizing problem with setup carry-over

Lot sizing is one of the most challenging problems in production planning. This paper focuses on the multi-plant capacitated lot sizing problem (MPCLSP) with setup carry-over (MPCLSP-SC). To the best of our knowledge, the MPCLSP-SC has been the subject of one single study, despite the fact that it can bring financial benefits, primarily by considering case studies under tight capacity. Moreover, in spite of the significant improvement in the performance of benchmark optimization solvers over the years, they are still very time and memory consuming for large MPCLSP instances. Therefore, heuristics stand out among the solution methods to tackle this type of problem. This paper introduces a novel matheuristic, a kernel search (KS) method, to solve the MPCLSP-SC. Through computational experiments, it was possible to show the outstanding performance of the proposed method for large instances with diverse characteristics. The experiments also demonstrate that the introduced KS outperformed state-of-the-art methods to solve the MPCLSP.

Simple Policies for Managing Flexible Capacity

In many scenarios, a fixed capacity is shared flexibly between multiple products. To manage such multiproduct systems, firms need to make two sets of decisions. The first one requires setting an inventory target for each product, and the second requires dynamically allocating the scarce capacity among the products. It is not known how to make these decisions optimally. In this paper, we propose easily implementable policies that have both theoretical and practical appeal. We first suggest simple and intuitive allocation rules that determine how such scarce capacity is shared. Given such rules, we calculate the optimal inventory target for each product. We demonstrate analytically that our policies are optimal under two asymptotic regimes represented by high service levels (i.e., high shortage costs) and heavy traffic (i.e., tight capacity). We also demonstrate that our policies outperform current known policies over a wide range of problem parameters. In particular, the cost savings from our policies become more significant as the capacity gets more restrictive. The online appendix is available at https://doi.org/10.1287/msom.2017.0648 .

A tabu search algorithm for a capacitated clustering problem

This paper investigates a clustering problem in a production and routing environment where a centralised facility uses a fleet of vehicles to serve a set of customers. A multi-period capacitated clustering problem is solved to partition customer into clusters with constraints that the accumulated customer demand in every period of the planning horizon is satisfied. The resulting mathematical model is hard to solve exactly and efficient heuristics are thus developed in this paper. The heuristics are based on the tabu search but include two novel features in the design of neighbourhood search; the first one is the dynamic combination of customers with close proximity into supernodes to eliminate ineffective moves and the second one is an ejection chain approach to perform composite moves of variable length from a series of simple moves. Computational results showed that they were able to provide superior solutions, especially in certain cases of tight capacity constraints.

A tabu search algorithm for a capacitated clustering problem

This paper investigates a clustering problem in a production and routing environment where a centralised facility uses a fleet of vehicles to serve a set of customers. A multi-period capacitated clustering problem is solved to partition customer into clusters with constraints that the accumulated customer demand in every period of the planning horizon is satisfied. The resulting mathematical model is hard to solve exactly and efficient heuristics are thus developed in this paper. The heuristics are based on the tabu search but include two novel features in the design of neighbourhood search; the first one is the dynamic combination of customers with close proximity into supernodes to eliminate ineffective moves and the second one is an ejection chain approach to perform composite moves of variable length from a series of simple moves. Computational results showed that they were able to provide superior solutions, especially in certain cases of tight capacity constraints.

A model-based approximation of opportunity cost for dynamic pricing in attended home delivery

For online retailers with attended home delivery business models, the decisive factor for promising dynamic time slot pricing decisions is the quality of the opportunity cost approximation concerning incoming customer requests. For this purpose, we present a novel approximation approach based on mixed-integer linear programming that we integrate into the de facto standard dynamic pricing framework prevalent in the academic literature. Our approximation combines the most current information regarding the customers accepted to date with a forecast of expected customers to come that is adapted during the progress of the booking horizon. Thus, future customer requests demand management, i.e. the consequences of future pricing decisions, is anticipated. We approximate the retailer’s vehicle routes and thus delivery costs of expected customers by a dynamic seed-based scheme in which potential seeds’ locations as well as related distance approximations are dynamically adjusted under consideration of the locations of already accepted customers. In a computational study, we compare the approach to established pricing approaches in practice and to the state-of-the-art dynamic pricing policy. We show that our approach constantly yields the highest profit, specifically given a tight capacity level. We further provide implications for practical use. We show that, even for large-scale implementations in a real-time environment, our approach is applicable by using parallel computing and by only periodically recalculating opportunity cost. Even then, our approach leads to very good results.

Outsourcing in place: Should a retailer sell its store-brand factory?

ABSTRACTSeveral major grocery chains in the United States own factories that produce some of their store-brand products. Historically, these store-brand products have been the low-price, lower-quality alternatives to higher-priced national brands, but the quality and consumer acceptance of store brands have increased markedly in recent years. Although demand for store-brand products has grown, managing the associated factories can be costly for retailers, leading some to consider selling the factories to third parties.We study the impact of selling a retailer’s existing capacity-limited factory to a third party when a store-brand product competes with a similar national-brand product. We examine the equilibrium dynamics between two external suppliers and show how the outcome changes with respect to prices, capacity limitations, the distribution of profits, and the sequencing of pricing decisions. Among other things, we show that, surprisingly, the national brand’s equilibrium wholesale price may fall when the factory is sold. We also show that the retailer may be strictly better off if he sells the factory, with these benefits being above and beyond any savings in fixed ownership and operating costs. Taken together, these results imply that when the store-brand factory has tight capacity, the adverse effects due to double marginalization on the store-brand product from selling the factory to a third party may be partially or fully offset by a reduction in the national brand’s wholesale price.

Power Allocation and Relay Selection for AF Cooperative Relay Systems With Imperfect Channel Estimation

In this paper, we investigate power-allocation and relay-selection algorithms for amplify-and-forward (AF) cooperative relay systems under imperfect channel estimation. Based on maximizing a tight capacity lower bound, a closed-form formula for optimal power allocation under a given relay is first derived and then used to establish an optimal relay-selection algorithm. By exploring the asymptotic behavior of the capacity lower bound, a suboptimal but reduced-complexity relay-selection algorithm is further presented. Simulation results demonstrate that significant capacity gains can be achieved by using the proposed optimal and suboptimal algorithms.

Capacitated transit service network design with boundedly rational agents

This paper proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. Within a time-discretized space-time network, the time-dependent transit services are characterized by traveling arcs and waiting arcs with constant travel times. Instead of using traditional flow-based formulations, an agent-based integer linear formulation is proposed to represent boundedly rational decisions under strictly imposed capacity constraints, due to vehicle carrying capacity and station storage capacity. Focusing on a viable and limited sets of space-time path alternatives, the proposed single-level optimization model can be effectively decomposed to a time-dependent routing sub-problem for individual agents and a knapsack sub-problem for service arc selections through the Lagrangian decomposition. In addition, several practically important modeling issues are discussed, such as dynamic and personalized transit pricing, passenger inflow control as part of network restraint strategies, and penalty for early/late arrival. Finally, numerical experiments are performed to demonstrate the methodology and computational efficiency of our proposed model and algorithm.

On the Approximation of Opportunity Cost for Dynamic Pricing in Attended Home Delivery

The decisive factor for promising dynamic time slot pricing decisions in attended home delivery is the quality of the opportunity cost approximation with regard to incoming customer requests. In this paper, we present a novel approximation based on mixed-integer linear programming that we integrate into the dynamic pricing approach recently proposed by [Yang X et al. (2014) Choice-based demand management and vehicle routing in e-fulfillment. Transp Sci. Articles in Advance. 1–16]. In line with these authors’ concluding suggestions, our approximation explicitly combines the most current information regarding the customers accepted to date with a forecast of expected customers to come which is adapted to the progress of the booking horizon. Thereby, future customer requests’ demand management is anticipated. Delivery costs are approximated based on a well-established seed-based scheme [Fisher ML, Jaikumar R (1981) A generalized assignment heuristic for vehicle routing. Netw 11(2):109–124]. In a computational study, we evaluate the proposed approach and compare it to established pricing approaches in practice, e.g. fix prices. We show that our approach constantly yields the highest profit, specifically given a tight capacity level of the service provider. Based on the computational study’s results, we provide implications for the practical use of the approach, especially when real-time pricing decisions are required.

Communication Over Finite-Chain-Ring Matrix Channels

Though network coding is traditionally performed over finite fields, recent work on nested-lattice-based network coding suggests that, by allowing network coding over certain finite rings, more efficient physical-layer network coding schemes can be constructed. This paper considers the problem of communication over a finite-ring matrix channel $Y = AX + BE$, where $X$ is the channel input, $Y$ is the channel output, $E$ is random error, and $A$ and $B$ are random transfer matrices. Tight capacity results are obtained and simple polynomial-complexity capacity-achieving coding schemes are provided under the assumption that $A$ is uniform over all full-rank matrices and $BE$ is uniform over all rank-$t$ matrices, extending the work of Silva, Kschischang and K\"{o}tter (2010), who handled the case of finite fields. This extension is based on several new results, which may be of independent interest, that generalize concepts and methods from matrices over finite fields to matrices over finite chain rings.