Operations Research and Enterprise Systems

Production frontier analysis aims at the identification of best production practices and the importance of external factors, endogenous or not, that affect the production function and the technical efficiency component. In particular, in the context of the Brazilian agriculture, it is desirable for policy makers to identify the effect on production of variables related to market imperfections. Market imperfections occur when farmers are subjected to different market conditions depending on their income. In general, large scale farmers access lower input prices and may sell their production at lower prices, thereby making competition harder for small farmers. Market imperfections are typically associated with infrastructure, environment control requirements and the presence of technical assistance. In this article, at county level, and using agricultural census data, we estimate the elasticities of these variables on production by maximum likelihood methods. Technological inputs dominate the production response, followed by labor and land. Environment control has a positive net effect on production, as well as technical assistance. The indicator of infrastructure affects positively technical efficiency. There is no evidence of technical assistance endogeneity.

Many markets are characterized by competitive settings and incomplete information. While offer prices of sellers are often observable, the competitors’ inventory levels are mutually not observable. In this paper, we study stochastic dynamic pricing models in a finite horizon duopoly model with partial information. To be able to derive effective pricing strategies when the competitor’s inventory level is not observable, we use a Hidden Markov Model. Our approach is based on feedback pricing strategies that are optimal, if the competitor’s inventory level is observable. Optimized price reactions are balancing two effects: (i) to slightly undercut the competitor’s price to sell more items, and (ii) to use high prices to promote a competitor’s run-out. For the case that a competitor’s strategy is unknown, we derive robust heuristic strategies. Comparing duopolies with different information structures, we find that expected sales results are quite similar as long as the firms’ information is symmetric. By evaluating asymmetric pairs of strategies, we study to which extent the value of additional information is affected by the consumers’ price sensitivity or the competitors’ price response times.

In this study, a Causal Bayesian network (CBN) model of the causal relationships between supply chain enablers, supply chain management practices and supply chain performances is empirically developed and analyzed. Study data collected from a sample of 199 manufacturing firms producing the most influential products in Iran’s economy. Resultant CBN model revealed important causalities between study variables of interest. Afterwards, using Dirichlet estimator of TETRAD 6-4-0 software, conditional probability estimation with Bayesian networks, also known as Bayesian inference was developed. The outcomes of this study in general, support the idea that SC enablers, especially IT technologies, don’t have direct impact on SC performance. Also forward Bayesian inference provided deeper understanding of causal relationships in supply chain context, such as what antecedents must be available to reach better level at each critical supply chain performance measures. Also it is found out that in any tier of supply chain concepts; there may be some important intra-relations which worth of further studies.

We introduce two specific design problems of optical fiber cable networks that differ by a practical maintenance constraint. An integer programming based method including valid inequalities is introduced for the unconstrained problem. We propose two exact solution methods to tackle the constrained problem: the first one is based on mixed integer programming including valid inequalities while the second one is built on dynamic programming. We then provide a fully polynomial time approximation scheme for the constrained problem. The theoretical complexities of both problems in several cases are proven and compared. Numerical results assess the efficiency of both methods in different contexts including real-life instances, and evaluate the effect of the maintenance constraint on the solution quality.

The present paper highlights the impact of heuristic hybridization on Vehicle Routing Problems (VRPs). More specifically, we focus on the hybridization of the Iterated Local Search heuristic (ILS). We propose different hybridization levels for ILS with two other heuristics, namely a Variable Neighborhood Descent with Random neighborhood ordering (RVND) and a Large Neighborhood Search heuristic (LNS). To evaluate the proposed approaches, we test them on a variant of VRPs called the Capacitated Profitable Tour Problem (CPTP). In a CPTP, the visit of all customers is no longer required and the visit of each customer generates a specific profit. The available fleet of vehicle is limited and capacitated. The aim of the CPTP is to choose which set of customers to visit and in which order to maximize the difference between collected profits and routing costs. Our experiments show that the more ILS is hybridized the better are the results. To bring out the effectiveness of the proposed hybrid approach combining ILS, RVND and LNS, a comparison is made between that proposed approach and three local search heuristics from the literature of the CPTP. The obtained results are competitive.

An effective spare part supply system planning is essential to achieve a high capital asset availability. We investigate the design problem of a repair shop in a single echelon repairable multi-item spare parts supply system. The repair shop usually consists of several servers with different skill sets. Once a failure occurs in the system, the failed part is queued to be served by a suitable server that has the required skill. We model the repair shop as a collection of independent sub-systems, where each sub-system is responsible for repairing certain types of failed parts. The procedure of partitioning a repair shop into sub-systems is known as pooling, and the repair shop formed by the union of independent sub-systems is called a pooled repair shop. Identifying the best partition is a challenging combinatorial optimization problem. In this direction, we formulate the problem as a stochastic nonlinear integer programming model and propose a sequential solution heuristic to find the best-pooled design by considering inventory allocation and capacity level designation of the repair shop. We conduct numerical experiments to quantify the value of the pooled repair shop designs. Our analysis shows that pooled designs can yield cost reductions by 25% to 45% compared to full flexible and dedicated designs. The proposed heuristic also achieves a lower average total system cost than that generated by a Genetic Algorithm (GA)-based solution algorithm.

A class of resource allocation problems is considered where some quality of service measure is set against the agent related costs. Three multiobjective minimization problems are posed, one for a system of Erlang-C queues and two for systems of Erlang-A queues.

In the case of the Erlang-C systems we introduce a quality of service measure based on the Conditional Value-at-Risk with waiting time as the loss function. This is a risk coherent measure and is well established in the field of finance. An algebraic proof ensures that this quality of service measure is integer convex in the number of servers.

In the case of the Erlang-A systems we introduce two different quality of service measures. The first is a weighted sum of fractions of abandoning customers and the second is Conditional Value-at-Risk, with the waiting time in queue for a customer conditioned on eventually receiving service. Finally, numerical experiments on the two system types with the given quality of service measures, are presented and the optimal solutions are compared.

Many real-world applications require decision-makers to assess the quality of solutions while considering multiple conflicting objectives. Obtaining good approximation sets for highly constrained many-objective problems is often a difficult task even for modern multiobjective algorithms. In some cases, multiple instances of the problem scenario present similarities in their fitness landscapes. That is, there are recurring features in the fitness landscapes when searching for solutions to different problem instances. We propose a methodology to exploit this characteristic by solving one instance of a given problem scenario using computationally expensive multiobjective algorithms to obtain a good approximation set and then using Goal Programming with efficient single-objective algorithms to solve other instances of the same problem scenario. We use three goal-based objective functions and show that on benchmark instances of the multiobjective vehicle routing problem with time windows, the methodology is able to produce good results in short computation time. The methodology allows to combine the effectiveness of state-of-the-art multiobjective algorithms with the efficiency of goal programming to find good compromise solutions in problem scenarios where instances have similar fitness landscapes.

We previously studied the capacitated arc routing problem over sparse underlying graphs under travel costs uncertainty. In this paper, we study the same problem by recalling the mathematical formulation of the problem given in [29]. The problem is characterized by the uncertainty of the travel costs and by the sparse network over which it is defined. In fact, a Multiple-Scenario Min-Max CARP over sparse underlying graphs is studied. More numerical instances applying the greedy heuristic algorithm developed in [29] and the adapted tabu-search algorithm are introduced in which these computational experiments show the effectiveness of these two algorithms.

Consider the air-cargo service chain which comprises a carrier and multiple forwarders. The carrier and each of the forwarders may establish an allotment contract at the start of the season. We formulate the contract design problem as a Stackelberg game, in which the carrier is the leader and offers a contract to a forwarder. The contract parameters may include the discount contract price and the penalty cost for the unused allotment as well as the minimum allotment utilization. The carrier’s contract is accepted, if the forwarder earns at least its reservation profit. Given the carrier’s offer, the forwarder decides how much to book as an allotment, in order to maximize its own expected profit. We show that the two-parameter contract suffices to coordinate the service chain, and the carrier earns the maximum chain’s expected profit less the total reservation profits of all forwarders. If the penalty cost is not imposed, then the minimum allotment utilization is needed to construct an efficient contract. On the other hand, if the penalty cost is strictly positive, then there is no need to impose the minimum allotment requirement.

Many systems undergoing an optimisation process also involve users which might be directly or indirectly impacted in different ways. Fairly spreading this positive or negative impact is required in specific contexts like critical healthcare or due to work regulation constraints. It can also be explicitly requested by users. This papers considers case studies from three different domains involving fairness: night shift planning, clinical pathways and a shared shuttle system. Each case is analysed to understand how fairness requirements were captured, how the solution was designed and implemented. It also analyse how fairness was perceived by the user using the deployed system. We also draw some lessons learned and recommendations which are discussed in the light of similar work reported in other domains.