Cost Parameter Values Research Articles

A benchmark dataset for the retail multiskilled personnel planning under uncertain demand

In this data article, we present and describe datasets designed to address multiskilled personnel assignment problems (MPAP) under uncertain demand. The data article introduces simulated datasets and a real dataset obtained from a retail store in Chile. The real dataset provides details on the structure of the store, including the number of departments and workers, the type of labor contract, the cost parameter values, and the average demand across all store departments. The simulated datasets, consisting of 18 categorized text files, were generated through Monte Carlo simulation to encapsulate information about the stochastic demand for store departments. These text files are classified based on: (i) type of sample (in-sample or out-of-sample), (ii) type of truncation method (zero-truncated or percentile-truncated), and (iii) demand coefficient of variation (5%, 10%, 20%, 30%, 40%, 50%). This categorization allows academics and practitioners to select the scenarios that meet with their specific research or application needs, increasing the flexibility and applicability of the datasets. In addition, researchers and practitioners can use these comprehensive real and simulated datasets to benchmark the performance of diverse optimization methods under uncertain demand, thereby ensuring robust multiskilling levels for similar MPAPs. Furthermore, we offer an Excel workbook with the capability to generate up to 10,000 demand scenarios for varying coefficients of variation in demand.

Procurement portfolio planning for a newsvendor with supplier delivery uncertainty

We consider the problem of allocating procurement quantities for a required input to multiple suppliers in advance of a selling season with uncertain demand. Planning complexity is compounded by uncertainty in whether each supplier will deliver on time, in addition to the existence of an option for reserving capacity with a perfectly reliable supplier and the presence of a spot market for the input with an uncertain price. We apply analytical methods including two-stage stochastic programming with recourse, nonlinear optimization, and exact and efficient model-based heuristic solution methods. We show that for a selected supplier, the newsvendor’s conditional service level, given that the supplier delivers on time, equals a critical ratio value determined by the supplier’s cost and reliability parameters. Post-optimality analysis reveals interesting insights on the way reliability and cost parameters interact to determine whether a supplier receives a positive order allocation, as well as the impact of payment schemes on supplier selection decisions. The analysis clarifies an insightful relationship between the conditional service level and a critical ratio value for each selected supplier. The results also sharpen insights on the way relative supplier reliability and cost parameter values interact to determine an optimal supply portfolio. Furthermore, the ability to interpret a perfectly reliable supplier with a capacity reservation cost as an equivalent unreliable supplier reveals additional insights on the relative value of capacity reservation and facilitates direct comparison among seemingly disparate supply options.

Deploying bioenergy for decarbonizing Malaysian energy sectors and alleviating renewable energy poverty

Due to the capital cost of co-firing being lower than other biomass technologies, the transformation of coal plants into co-firing facilities can potentially minimize the bioenergy cost needed to meet energy decarbonization targets. This study analyzes the impact of the co-deployment of co-firing and dedicated biomass technologies in contributing to the bioenergy cost reduction for country-level energy systems using a spatio-temporal techno-economic optimization model. Malaysia is used as a case in the analysis. Different scenarios were developed to assess the robustness of the cost reduction potential under the impact of incremental CO2 reduction targets and supply chain cost parameter variations. Our results suggest that the multi-sectoral deployment of bioenergy in energy systems is key to meeting decarbonization targets at the national scale. By also considering co-firing in the biomass technological pathway, up to 27% of bioenergy cost reduction can be enabled in the baseline case. The decrease in the supply chain cost parameter values further enhances the cost reduction potential; bioenergy costs can be reduced up to threefold. The findings have shown that developing countries such as Malaysia can benefit from the use of their rich agricultural resources to cost-effectively alleviate renewable energy poverty.

Fuzzy production-inventory models with shortages via ranking technique and credibility measure

In this paper, a production-inventory model for a defective/deteriorating item is developed with imprecise inventory cost parameters. Here, we have used two defuzzification techniques: 1) valuation/weighting function of Yager's ranking method; 2) credibility measure, a compromise between optimistic and pessimistic measures for the deterministic transformation of the proposed model. A weighting function is added to Yager's ranking method so that it can represent the decision preference to solve a production inventory model. The proposed fuzzy inventory models are reduced to the crisp ones applying credibility measure. The problems are formulated as cost minimisation problems and solved using a gradient-based nonlinear programming technique. Finally, the models are illustrated through a numerical experiment with triangular fuzzy cost parameters. The results from two methods are compared and sensitivity analysis for different values of cost parameters and confidence levels (in credibility measure) is presented.

Hierarchical Three-Way Decisions With Intuitionistic Fuzzy Numbers in Multi-Granularity Spaces

Intuitionistic fuzzy sets, as extensions of fuzzy sets, are described by intuitionistic fuzzy numbers (IFNs) that describe the uncertain concepts with the membership and non-membership degrees together. Three-way decisions are effective classification methods, which are always utilized for solving uncertain problems with fixed cost parameter values and fixed attribute values in reality. However, under dynamic intuitionistic fuzzy environments, three-way decisions face a great challenge for processing uncertain problems with IFN cost parameters and IFN attribute values. In this paper, by considering the impact of IFNs, a hierarchical three-way decision model with IFN cost parameters (H3WDIF-I) is established in multi-granularity spaces for handling dynamic and uncertain problems first. The change rules of H3WDIF-I are discussed to analyze the relationships of decisions based on the granularity structures. In addition, when the IFN cost parameters and IFN attribute values arise together, a hierarchical three-way decision model with IFNs (H3WDIF-II) is proposed to explore three-way decisions in depth under dynamic intuitionistic fuzzy environments. Similarly, the change rules of H3WDIF-II are also discussed from the viewpoint of classification losses in multi-granularity spaces. Finally, the presented change rules are verified by many examples and experiments.

Consumer Search Costs and Preferences on the Internet

We conduct an empirical analysis of consumer preferences and search costs on an Internet platform. Using data from a major French platform (PriceMinister), we show descriptive evidence of substantial price dispersion among adverts for the same product, of consumers often not choosing the cheapest advert and sometimes choosing an advert dominated in price and non-price characteristics by another available advert. We consider a sequential search model where consumers sample adverts in an endogenous order based on their preferences and search costs. We show that the optimal search-and-purchase strategy can be characterized by a set of inequalities which can feasibly be tested on transaction and advert data. This allows us to estimate, for each transaction, a set of preference and search cost parameter values, thus allowing for flexible consumer heterogeneity in preferences and search costs. The estimated model can then describe a wide range of consumer search and purchase behaviours. We find that the model can explain almost all transactions in the data and requires non-zero preferences and search costs for at least 50% and 22% (respectively) of observations. We also find evidence of heterogeneous and sometimes substantial search costs.

On the viability of partial inventory pooling

When demands are uncertain, complete inventory pooling is known to be a better strategy than no pooling if the trans-shipment cost is low enough; otherwise, pooling is undesirable. But are there situations where pooling some inventories, but not the rest, in a central warehouse, are the best strategy? We show that such is indeed the case for certain demand distributions and cost parameter values. We show it for a symmetric discrete demand scenario, as well as for a symmetric continuous demand scenario.

On the viability of partial inventory pooling

When demands are uncertain, complete inventory pooling is known to be a better strategy than no pooling if the trans-shipment cost is low enough; otherwise, pooling is undesirable. But are there situations where pooling some inventories, but not the rest, in a central warehouse, are the best strategy? We show that such is indeed the case for certain demand distributions and cost parameter values. We show it for a symmetric discrete demand scenario, as well as for a symmetric continuous demand scenario.

Travel time variations over time and routes: endogenous congestion with degradable capacities

ABSTRACTTo investigate how the travel time variability develops over time and routes, in relation to mean travel time, this paper analytically explores the departures with day-to-day capacity variations. Congestion is treated as an endogenous phenomenon. An either three-regime or two-regime solution case is defined, dependent on a combined effect of different capacity variations and cost parameter values. Rigorous formulations and condition of each solution situation are provided. A more complete trip making process is modelled as well considering joint route and departure time decisions of travellers in response to uncertainty. Examples with different capacity variations and cost parameters are presented with solutions graphically shown, for both single- and parallel-bottleneck models. It appears that larger capacity variation yields higher travel cost to travellers due to higher early schedule delay cost and unreliability cost. Travel time unreliability cost is not neglectable in transport economics appraisal. In a long-time period, travel time variability is increasing over time, while mean travel time is decreasing at the same time. There is no evidence on the proportional relations between travel time variability and mean travel time over time and routes.

Cost effectiveness of renal denervation therapy for the treatment of resistant hypertension in the UK.

Patients with resistant hypertension are at a high risk for developing serious cardiovascular events and renal complications. Catheter-based renal denervation (RDN) is a procedure with the potential to normalize systolic blood pressure (SBP). The overall objective of the study was to estimate the cost effectiveness of RDN in the UK for patients with diagnosed resistant hypertension, expressed as a standard cost per quality-adjusted life-year (QALY) ratio. A patient lifetime, economic, Markov heath-state model was developed, linking expected changes in SBP to reductions in risks for cardiovascular events and renal complications, using the Framingham, PROCAM, and other published risk equations. The model was developed from the perspective of the healthcare payer in the UK using relevant cost data from 2012. Clinical effectiveness for RDN (a mean reduction of 32 mmHg in SBP) was taken from the phase III Symplicity HTN-2 trial, in patients with a mean baseline SBP of 178 mmHg. HTN-2 was the largest, multicenter randomized controlled trial on the effectiveness of RDN therapy at the time of the model development. A systematic review identified UK-specific sources for utility, mortality, and cost parameter values, and included recently published UK guidelines for the clinical management of hypertension. RDN therapy resulted in an increase in health benefit over a patient's lifetime compared with anti-hypertensive pharmacological treatment alone (12.77 vs. 12.16 QALYs; discounted). Additional lifetime costs per patient were modeled at £2,961; equivalent to an incremental cost per additional QALY of £4,805. This result was robust to full probabilistic sensitivity and scenario analyses. RDN is an effective clinical procedure that offers patients a meaningful and cost-effective alternative for achieving SBP control, where traditional combination, anti-hypertensive pharmacologic strategies have been proven to be ineffective.

IP/MPLS-over-OTN-over-DWDM Multilayer Networks: An Integrated Three-Layer Capacity Optimization Model, a Heuristic, and a Study

Multilayer network design has received significant attention in current literature. Despite this, the explicit modeling of IP/MPLS over OTN over DWDM in which the OTN layer is specifically considered has not been addressed before. This architecture has been identified as promising that bridges integration and interaction between the IP and optical layers. In this paper, we present an integrated capacity optimization model for network planning of such multilayer networks that consider the OTN layer as a distinct layer with its unique technological sublayer constraints. We develop a heuristic algorithm to solve this model for large networks. Finally, we provide a detailed numeric study that considers various cost parameter values of each layer in the network. We analyze the impact of each layer's cost parameter values on neighboring layers and overall network cost.

The cost allocation issue in joint replenishment

Joint replenishment for several products to achieve a lower inventory logistics cost has been a topic of extensive studies. Less attention has been paid to the issue of deciding how the joint replenishment costs should be allocated across the individual products. Ideally, when items are ordered together one would require a stable cost allocation, such that no subset of products subsidizes another subset. When part of each product's ordering cost is product specific and part can be shared with other products (like in a 3PL setting of Anily and Haviv), it has been shown that even when a stable allocation exists, such an allocation might be difficult to compute. In addition, usually the components of ordering costs are partially determined using estimates and accounting discretion. This paper provides two main insights for determining an appropriate cost allocation. It provides the means to test how sensitive a stable cost allocation is to a range of cost parameter values. Then, in a computational study, it is shown how to obtain a stable cost allocation without excessive computation.

Dynamic Multipriority Patient Scheduling for a Diagnostic Resource

We present a method to dynamically schedule patients with different priorities to a diagnostic facility in a public health-care setting. Rather than maximizing revenue, the challenge facing the resource manager is to dynamically allocate available capacity to incoming demand to achieve wait-time targets in a cost-effective manner. We model the scheduling process as a Markov decision process. Because the state space is too large for a direct solution, we solve the equivalent linear program through approximate dynamic programming. For a broad range of cost parameter values, we present analytical results that give the form of the optimal linear value function approximation and the resulting policy. We investigate the practical implications and the quality of the policy through simulation.

A two-level approach to choose the cost parameter in support vector machines☆

A new SVM model used to calculate the optimal value of cost parameter C for particular problems of linearity non-separability of data is presented in this paper. The new SVM model is formulated in the form of one of MPEC problems with an integer objective function. A lower bound, positive number, C0 is required to provide for avoiding choosing a candidate set of C. Numerical experiments show that this model for choice of C is suitable for solving SVM problems.

On vendor part delivery dates in a stochastic assembly system

This paper describes the development of a model for the determination of optimal mean part delivery dates in a stochastic assembly system for the objective of minimizing the expected cost of subassembly and part inventory. Parts are assembled at each station to a subassembly. The part delivery and processing times at assembly stations follow known probability distributions. An approximate solution technique based on the optimization of individual stations in isolation is developed. The approximation applies a correction factor, as a function of the variability in part delivery and processing time, cost parameters and number of stations, to the decisions from the single station solutions to compensate for interdependence between stations. Results indicate that this is an effective approach and yields good near-optimal solutions with very little computational effort. Insights regarding the effect of the type of distribution used, random processing times, variance of the distribution used and cost parameter values on part delivery dates are also reported.

A network flow model for the capacitated lot-sizing problem

The lot-sizing problem considered in this paper consists in planning the production of multiple items on a single machine over a finite planning horizon divided into time periods. The objective of the problem is to determine a minimum cost production plan that meets the forecast demand for the items. The mathematical model considers setup time and setup cost, and is represented as a minimum cost network flow problem. A branch-and-bound method is proposed for solving the model. The performance of the method is evaluated by using numerical experiments for various demand patterns and values of cost parameters.

Dimensioning of survivable WDM networks

In this paper routing, planning of working capacity, rerouting, and planning of spare capacity in wavelength division multiplexing (WDM) networks are investigated. Integer linear programming (ILP) and simulated annealing (SA) are used as solution techniques. A complex cost model is presented. The spare capacity assignment is optimized with respect to three restoration strategies. The benefit of wavelength conversion, the choice of the fiber line system, and the influence of cost parameter values are discussed, with respect to the different restoration strategies and solution techniques. Wavelength conversion is found to be of limited importance, whereas tunability at the end points of the connections has substantial benefits.

A heuristic lot sizing algorithm for a GT cell

In this paper, a multi-group single-level capacitated dynamic lot sizing problem for a GT cell is formulated as a mixed integer programming problem. In the model, group setup time, part setup time, and the effects of setup time on production capacity are discussed in detail. A two-stage heuristic lot sizing algorithm consisting of a cost reduction stage and a feasibility enforcement stage in developed and illustrated by a numerical example. The performance of the proposed algorithm is evaluated using the numerical experiment for various demand patterns and values of cost parameters.

An algorithm for selecting an optimal acceptance plan in quality control and auditing

This paper is concerned with the computational determination of minimum cost parameter values (sample size and acceptance number) for single sample. Bayesian acceptance plans in quality control when the prior distribution of lot quality and the sampling distribution are discrete. The cost surface of such problems can be described as a discrete, discontinuous function with numerous local optima. A two-stage optimization algorithm is developed for determining the optimal economic sampling plan. The effectiveness of this algorithm is systematically evaluated and shown to be very efficient both in terms of solution quality and computational time against existing solution procedures.

A program of a generalized Bayesian algorithm for quality control and auditing

The algorithm described below determines the minimum cost parameter values (sample size n* and ac~eptance nUJ?ber c*) for single sample acceptance plans which incorporate mspec!or errors in quality control and auditing problems w~en th~ p~or distribution of lot or account quality and sampling distribution are discrete. In quality control, for example, such plans involve: (1) drawing a single sample of n items from a lot of size N, (2) determining the number of defective items, y, in the sample (perhaps erroneously observed ~d reported), and (3) rejecting the lot if more than c defectives are observed. In principle, three different approaches can be used to determine the parameter values for an error prone (or erro.r fr~e) single sample attribute acceptance plan when a cost crlten.on is applied. First, Bayesian decision tree methods can be .appbed to determine an exact optimal solution by complete implicit enumeration of all possible solutions (Raiffa & Schlaifer, 1961). However, this involves substantial computational effort, since all of the branches of the decision tree must be evaluated and the computations of the various probabilities, if in~pector error is involved can become onerous. Second, analytical methods can be used when the distribution of the lot fraction defective can be approximated by a standard density function such as the Beta or Polya distribution (Collins, 1974, and references cited therein). Although this procedure reduces the computational requirements, nonoptimal solutions to the real problem may be produced as a result of these approximations, since the densities may not be sufficiently rich to adequately capture the true prior distribution. Finally, direct num~rical search techniques can be used to reduce the computational effort, but such methods may not converge on an optimal solution because of the bumpy nature of the cost surface in such problems. The programmed algorithm described below is a general algorithm that determines the optimal par~meter values for Bayesian single sampling attribute plans With (or without) inspector error. It has been shown to be clearly superior to the above alternate approaches, produ~ing the optimal solution with minimal computational requirements over a wide range of problem types (Moskowitz & Fink, 1975). The algorithm is applicable to a broad range of acceptance sampling and auditing problems, assuming only that the sampling or auditing cost is either a linear or strictly convex function of the sample size. The algorithm can accommodate both constant as well as nonconstant inspector errors, which, for example, might reflect the effects of fatigue on inspector performance. Description. The program of the algorithm consists of three principal aspects: (1) recursion routines for generating the probability matrices relating the quality states (F), actual defectives (X), and reported or adjudged defectives (Y) for the sample