The vehicle routing problem

The vehicle scheduling problem, arising in public transport bus companies, addresses the task of assigning buses to cover a given set of timetabled trips with consideration of practical requirements such as multiple depots and vehicle types as well as further extensions. An optimal schedule is characterized by minimal fleet size and/or minimal operational costs. Various publications were released as a result of extensive research in the last decades on this topic. Several modeling approaches as well as specialized solution strategies were presented for the problem and its extensions. This paper discusses the modeling approaches for different kinds of vehicle scheduling problems and gives an up-to-date and comprehensive overview on the basis of a general problem definition. Although we concentrate on the presentation of modeling approaches, also the basic ideas of solution approaches are given.

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An overview on vehicle scheduling models

A branch-and-price algorithm for the capacitated vehicle routing problem with stochastic demands

DEA cross-efficiency evaluation considering undesirable output and ranking priority: a case study of eco-efficiency analysis of coal-fired power plants

The capacitated arc-routing problem with stochastic demands (CARPSD) is an extension of the well-known capacitated arc-routing problem (CARP) in which demands are stochastic. This leads to the possibility of route failures whenever the realized demand exceeds the vehicle capacity. This paper presents the CARPSD in the context of garbage collection. It describes an adaptive large-scale neighbourhood search heuristic for the problem. Computational results show the superiority of this algorithm over an alternative solution approach.

An Adaptive Large Neighbourhood Search Heuristic for the Capacitated Arc-Routing Problem with Stochastic Demands

In this paper, we present a multi-faceted approach for ranking dispatching rules (DRs) in multi-objective dynamic flow shop scheduling systems using data envelopment analysis (DEA). The merits of t...

Ranking dispatching rules in multi-objective dynamic flow shop scheduling: a multi-faceted perspective

Achieving a supply chain that is resilient to potential unforeseen disruptions (e.g., strikes, floods, tsunamis, etc.) remains one of the vital concerns of decision makers (DMs). To build up a reactive supply chain plan towards resilience, the purchasing department needs to pay the strictest attention to sourcing decisions. This study contributes to the literature through developing an efficient resilient supplier selection approach based on a new holistic framework that enables the identification of key resilience pillars (RPs) and traditional business criteria (TBC) in light of a thorough literature review and experts’ opinions. To this end, the relative importance of TBC/RP was measured by applying the DEMATEL (D) method. This was followed by the application of MABAC-OCRA-TOPSIS-VIKOR (MOTV) methods to verify the suppliers’ ranking. Furthermore, the Spearman rank correlation coefficient (SRCC) approach was used to investigate the correlation among the suppliers’ ranking, revealed via the four methods. In this work, a real sourcing problem of scrap metal for a steel manufacturing company was solved to prove the applicability of the proposed approach. The research outcome revealed that the TBC of “trust” is the most important criterion, followed by the “cost”, leaving the “geographical location” criterion as the least important one. In this context, the RP of “flexibility” attained the highest relative weight compared to “agility”, which secured the lowest weight. The results also showed “absolute” correlation among MABAC, VIKOR, and OCRA compared to “very strong” correlation between TOPSIS and the others. This research can support supply chain managers to achieve supply chain systems that reduce not only sourcing costs, but also potential losses because of disrupting threats, by building resilient supply chains.

A Hybrid MCDM Approach towards Resilient Sourcing

Investigating the efficiency of greenhouse production in Oman: A two-stage approach based on Data Envelopment Analysis and double bootstrapping

A new methodology for assessing water quality, based on data envelopment analysis: Application to Algerian dams

Under a data envelopment analysis (DEA) framework, full ranking of a group of decision making units (DMUs) can be carried out through an adequate amalgamation of the cross-efficiency (CE henceforth) scores produced for each DMU. In this paper, we propose a ranking procedure that is based on amalgamating the weight profiles selected over the cross-evaluation rather than related CE scores. The new approach builds, for each DMU, a collective weight profile (CWP henceforth) by exploiting the preference voting system embedded within the matrix of weights, which views the assessing DMUs as voters and the input/output factors as candidates. The occurrence of zero votes is discussed as a special case and a two-level aggregation procedure is developed. The CWPs that are produced extend the concept of collective appreciation to the input/output factors of each DMU so that group dynamics is truly reflected, mainly in decision making circumstances where factor prioritization is necessary for making choices or allocating resources. The robustness of the proposed ranking approach is evaluated with three examples drawn from the literature.

Amar Oukil

Papers

Ranking dispatching rules in multi-objective dynamic flow shop scheduling: a multi-faceted perspective

A Hybrid MCDM Approach towards Resilient Sourcing

Investigating the efficiency of greenhouse production in Oman: A two-stage approach based on Data Envelopment Analysis and double bootstrapping

A new methodology for assessing water quality, based on data envelopment analysis: Application to Algerian dams

Exploiting value system multiplicity and preference voting for robust ranking