International Transactions in Operational Research

Publisher:: Wiley
Publication date:: 2021-02-01

ISBN:: 0969-6016

Issue Number

Latest documents

A two‐period pricing model with intertemporal and horizontal reference price effects
Consumers tend to compare the current price with historical prices of the same brand and selling prices of other brands, when they make purchase decisions. The intertemporal and horizontal reference price effect (RPE), formed by the historical price and the competitor's price, respectively, should be taken into account when developing optimal pricing strategies over several periods and in a competitive environment. This paper considers a two‐period pricing problem with two competing sellers, incorporating both types of RPE. We first develop a duopoly game to study the impacts and interactions of RPE of different types. Then we study a practice of price commitment when one firm gives up dynamic pricing. We find different types of RPEs have distinct impacts. The intertemporal RPE (IRPE) leads to a Hi‐Lo pricing strategy, while the horizontal RPE (HRPE) drives the selling prices of both periods downward. The IRPE is weakened by the HRPE, while the HRPE is weakened (intensified) in the first (second) period as the IRPE becomes stronger. Also we find price commitment is not beneficial. On the contrary, both firms may be worse off if one firm makes price commitment. Furthermore, if one firm decides to make price commitment he should announce the selling price well in advance.
A case study of consistent vehicle routing problem with time windows
We develop a heuristic for the consistent vehicle routing problem with time windows (ConVRPTW), which is motivated by a real‐world application at a food company's distribution center. Besides standard VRPTW restrictions, ConVRPTW assigns each customer just one driver to fulfill his or her orders during the whole multiperiod planning horizon. For each driver and period, a route is sought to serve all their customers with positive demand. For each customer, the number of periods between consecutive orders and the ordered quantities is highly irregular. This causes difficulties in the daily routing, negatively impacting the service level of the company. Similar problems have been studied as ConVRP, where the number of drivers is fixed a priori, and only the total travel time is minimized. Moreover, the clients present no time window constraints, but the visits should be scheduled with a small arrival time variation. In our model, the objective is to minimize the number of drivers. We impose hard time windows but do not consider time consistency in more detail. We compare solutions given by the heuristic with solutions of a mixed‐integer linear programming model on a set of small artificial instances and solutions used by the food company on real‐world instances. The results show the effectiveness of the heuristic. For the company, we obtain significant improvements in the routing plans, with a lower number of vehicles and a higher rate of orders delivered within the prescribed time window.
Coordinating the retail supply chain with item‐level RFID and excess inventory under a revenue‐cost‐sharing contract
This paper considers a supply chain consisting of one supplier and one retailer who jointly invest in item‐level radio frequency identification (RFID) by considering their demands and inventories. Since the supplier can be negative to adopt RFID, the main objective of this study is to design an effective mechanism that maximizes the profits of both players and the whole supply chain by implementing item‐level RFID. For this objective, we propose a novel revenue‐cost‐sharing (RCS) contract based on the bargaining game as the incentive to encourage the implementation, and numerical results show that the RCS contract is considerably more effective than the wholesale‐price contract for supply chain coordination. Moreover, we show that the retail supply chain can be coordinated perfectly under the RCS contract when the thresholds of the revenue/cost sharing rates are set appropriately. This study provides significant managerial insight into the incentive and coordination strategy to implement item‐level RFID in the retail supply chain.
Multiple criteria assessment of sustainability programs in the textile industry
To survive in the long term, business needs to profit, controlling environmental impacts with social responsibility. Sustainability programs involve the integration of social and environmental issues in business models and organizational processes. The assessment of sustainability programs is a problem of multiple criteria decision analysis (MCDA). This work presents applications of MCDA for the assessment of sustainability programs in the textile industry. Applied methods for MCDA are analytic hierarchy process (AHP) and the technique for the order of preference by similarity to ideal solution (TOPSIS). The reasons to apply AHP and TOPSIS include providing an assessment index, ranging from 0 to 1, and that the MCDA model is expected to have more criteria than alternatives. Therefore, an application of other methods, such as data envelopment analysis, could be prejudiced. Concepts from the triple bottom line, economic, social as well as environmental criteria were inserted in the proposed model. Sustainability programs of six leading companies from the Brazilian textile industry were evaluated. The main finding of the research is that AHP and TOPSIS resulted in similar evaluations for sustainability programs. Both methods resulted in the same rank of alternatives. However, with TOPSIS, companies' sustainability indices were more disperse, varying from 0.10 to 0.92 against a range from 0.23 to 0.69 with AHP.
Logical rules characterization of online consumer trust
In this study, we address the discriminant factors of website trust. We specifically build sets of propositional rules that can be used to predict the level of trustworthiness of a site. Focusing on initial trust, a survey was designed to assess site characteristics observed by the respondent and his/her perceptions around appearance, reputation, fulfillment, and security. By exploring data, we look for the most favorable rules classifiers among decision trees as well as classical and dominance‐based rough sets. A heuristic aiming to derive simpler classifiers is also proposed. The experimental setup considers diverse groups of attributes (predictors) for the extraction of rules. Results obtained are compared by taking into account predictive ability and parsimony of rules’ sets. Finally, the selected sets help bring light on how consumers process site information and suggest specific recommendations for e‐commerce vendors.
A continuous districting model focusing on intra‐ and inter‐zonal squared distances and its Voronoi‐based heuristic
We consider the problem of dividing a given convex polygon into p convex polygons called zones, each of which receives a designated land area. The position and shape of each zone is determined so that intra‐ and inter‐zonal trips for the resulting zones are conducted efficiently. To evaluate the compactness of the resulting zones, we derive the average squared distance between two points uniformly distributed in each zone, as well as the average squared distance between two points uniformly distributed in two zones. The weighted sum of these measures is used as the objective function, and a Voronoi‐based heuristic algorithm is proposed that iteratively updates the positions of p generator points placed inside a convex polygon. The method is used to divide several regular polygons, and the results show that zones become (i) rounded when intra‐zonal trips are prioritized and (ii) elongated with longer boundary lines when inter‐zonal trips are prioritized.
Absolute bounds of list algorithms for parallel machines scheduling with unavailability periods
We address the problem of scheduling a set of independent tasks on m parallel and identical machines subject to an arbitrary number of unavailability periods. The objective is to minimize the makespan. The problem was already investigated and was proved to be strongly NP-hard. Moreover, when the number of unavailability periods per machine exceeds 1, there is no bound for the problem. We proved the existence of tight absolute bounds when there is only one unavailability period per machine. In the general case, we proposed an efficient binary linear program and proved that the list algorithm based on the largest processing time (LPT rule) has a high performance. This study was motivated by grid scheduling, where a large number of independent jobs is sent to computing resources whose availability cannot be guaranteed.
Bicriterion scheduling with truncated learning effects and convex controllable processing times
This paper investigates single‐machine scheduling in which the processing time of a job is a function of its position in a sequence, a truncation parameter, and its resource allocation. For a convex resource consumption function, we provide a bicriteria analysis where the first is to minimize total weighted flow (completion) time, and the second is to minimize total resource consumption cost. If the weights are positional‐dependent weights, we prove that three versions of considering the two criteria can be solved in polynomial time, respectively. If the weights are job‐dependent weights, the computational complexity of the three versions of the two criteria remains an open question. To solve the problems with job‐dependent weights, we present a heuristic (an upper bound) and a branch‐and‐bound algorithm (an exact solution).
Issue Information
Polarization reduction by minimum‐cardinality edge additions: Complexity and integer programming approaches
Real‐world networks are often extremely polarized because the communication between different groups of vertices can be weak and, most of the time, only vertices within the same group or sharing the same beliefs communicate to each other. In this work, we introduce the minimum‐cardinality edge addition problem (MinCEAP) as a strategy for reducing polarization in real‐world networks based on a principle of minimum external interventions. We present the problem formulation and discuss its complexity, showing that its decision version is NP‐complete. We also propose three integer programming formulations for the problem and discuss computational results on artificially generated and real‐life instances. Randomly generated instances with up to 1000 vertices are solved to optimality. On the real‐life instances, we show that polarization can be reduced to the desired threshold with the addition of a few edges. The minimum intervention principle and the methods developed in this work are shown to constitute an effective strategy for tackling polarization issues in practice in social, interaction, and communication networks, which is a relevant problem in a world characterized by extreme political and ideological polarization.